Blood pressure and autoregulation monitoring

ABSTRACT

In some examples, a method includes receiving a signal indicative of a blood pressure of a patient and identifying at least one first portion of the signal comprising a first characteristic of the signal exceeding a first threshold. The method also includes identifying at least one first portion of the signal comprising a second characteristic of the signal exceeding a second threshold, the first characteristic being different than the second characteristic. The method further includes determining a filtered signal indicative of the blood pressure of the patient by excluding the at least one first portion and the at least one second portion from the signal. The method includes determining a set of mean arterial pressure values based on the filtered signal and determining an autoregulation status of the patient based on the set of mean arterial pressure values.

TECHNICAL FIELD

This disclosure relates to physiological parameter monitoring.

BACKGROUND

Cerebral autoregulation (CA) is the response mechanism by which anorganism regulates cerebral blood flow over a wide range of systemicblood pressure changes through complex myogenic, neurogenic, andmetabolic mechanisms. Autoregulation dysfunction may result from anumber of causes including, stroke, traumatic brain injury, brainlesions, brain asphyxia, or infections of the central nervous system.Intact cerebral autoregulation function occurs over a range of bloodpressures defined between a lower limit of autoregulation (LLA) and anupper limit of autoregulation (ULA).

A physiological monitoring device can determine the autoregulationstatus of a patient based on the mean arterial blood pressure (MAP) ofthe patient. The device can determine the MAP as the average of thearterial blood pressure over a cardiac cycle. MAP can be used as anindicator of perfusion, and blood pressures are commonly managed byclinicians in the operating room.

As the left ventricle contracts to push blood into the aorta andarterial system, arterial pressure increases until it reaches itsmaximum, known as the systolic pressure, before decreasing during thediastole until it reaches minimum pressure, known as the diastolicpressure, thus completing a cardiac cycle. Arterial blood pressure isdependent on the cardiac output from the heart and on the resistance ofthe cardiovascular system. The exact MAP value depends on a number offactors, such as the heart rate and shape of the pulse.

SUMMARY

This disclosure describes devices, systems, and techniques fordetermining mean arterial pressure values in the context ofautoregulation monitoring. An example device of this disclosure includesprocessing circuitry configured to receive a signal indicative of ablood pressure of a patient and determine the mean arterial pressurevalues based on the signal. In some examples, the processing circuitryis configured to identify a portion of the signal comprising one or morecharacteristics that exceed a respective threshold. The processingcircuitry may then exclude or modify the identified portion of thesignal to determine a filtered signal. The processing circuitry may thendetermine the mean arterial pressure values based on the filteredsignal.

In some examples, a device includes processing circuitry configured toreceive a signal indicative of a blood pressure of the patient andidentify at least one first portion of the signal comprising a firstcharacteristic of the signal exceeding a first threshold. The processingcircuitry is also configured to identify at least one second portion ofthe signal comprising a second characteristic of the signal exceeding asecond threshold, the first characteristic being different than thesecond characteristic. The processing circuitry is further configured todetermine a filtered signal indicative of the blood pressure of thepatient by excluding the at least one first portion and the at least onesecond portion from the signal. The processing circuitry is configuredto determine a set of mean arterial pressure values based on thefiltered signal and determine an autoregulation status of the patientbased on the set of mean arterial pressure values.

In some examples, a method includes receiving, by processing circuitry,a signal indicative of a blood pressure of a patient and identifying, byprocessing circuitry, at least one first portion of the signalcomprising a first characteristic of the signal exceeding a firstthreshold. The method also includes identifying, by processingcircuitry, at least one first portion of the signal comprising a secondcharacteristic of the signal exceeding a second threshold, the firstcharacteristic being different than the second characteristic. Themethod further includes determining, by the processing circuitry, afiltered signal indicative of the blood pressure of the patient byexcluding the at least one first portion and the at least one secondportion from the signal. The method includes determining, by processingcircuitry, a set of mean arterial pressure values based on the filteredsignal and determining, by processing circuitry, an autoregulationstatus of the patient based on the set of mean arterial pressure values.

In some examples, a device includes a display, sensing circuitryconfigured to generate a signal indicative of the blood pressure of thepatient, and a memory configured to store a first threshold for a firstcharacteristic of the signal and a second threshold for a secondcharacteristic of the signal. The device also includes processingcircuitry configured to identify at least one first portion of thesignal comprising the first characteristic of the signal exceeding thefirst threshold. The processing circuitry is also configured to identifyat least one second portion of the signal comprising the secondcharacteristic of the signal exceeding the second threshold, the firstcharacteristic being different than the second characteristic. Theprocessing circuitry is further configured to determine a filteredsignal indicative of the blood pressure of the patient by excluding theat least one first portion and the at least one second portion from thesignal. The processing circuitry is configured to determine a set ofmean arterial pressure values based on the filtered signal and determinean autoregulation status of the patient based on the set of meanarterial pressure values.

In some examples, a device includes a computer-readable medium havingexecutable instructions stored thereon, configured to be executable byprocessing circuitry for causing the processing circuitry to receive asignal indicative of a blood pressure of the patient and identify atleast one first portion of the signal comprising a first characteristicof the signal exceeding a first threshold. The instructions furthercause the processing circuitry to identify at least one second portionof the signal comprising a second characteristic of the signal exceedinga second threshold, the first characteristic being different than thesecond characteristic. The instructions also cause the processingcircuitry to determine a filtered signal indicative of the bloodpressure of the patient by excluding the at least one first portion andthe at least one second portion from the signal. The instructions alsocause the processing circuitry to determine a set of mean arterialpressure values based on the filtered signal and determine anautoregulation status of the patient based on the set of mean arterialpressure values.

In some examples, a method includes receiving, by processing circuitry,a signal indicative of a blood pressure of a patient and identifying, byprocessing circuitry, at least one first portion of the signalcomprising a first characteristic of the signal exceeding a firstthreshold. The method also includes identifying, by processingcircuitry, at least one first portion of the signal comprising a secondcharacteristic of the signal exceeding a second threshold, the firstcharacteristic being different than the second characteristic. Themethod further includes determining, by the processing circuitry, afiltered signal indicative of the blood pressure of the patient bymodifying the at least one first portion and the at least one secondportion from the signal. The method includes determining, by processingcircuitry, a set of mean arterial pressure values based on the filteredsignal and determining, by processing circuitry, an autoregulationstatus of the patient based on the set of mean arterial pressure values.

In some examples, a method includes receiving, by processing circuitry,a signal indicative of a blood pressure of a patient and identifying, byprocessing circuitry, at least one portion of the signal comprising acharacteristic of the signal indicating that the patient is undergoing acardiopulmonary bypass procedure. The method further includesdetermining, by the processing circuitry, a set of mean arterialpressure values based on the signal and determining, by processingcircuitry, a subset of the set of mean arterial pressure values for theat least one portion of the signal based on a moving average of thesignal in response to determining that the characteristic of the signalindicates that the patient is undergoing the cardiopulmonary bypassprocedure. The method includes determining, by processing circuitry, anautoregulation status of the patient based on the set of mean arterialpressure values and the subset of the set of mean arterial pressurevalues for the at least one portion of the signal.

In some examples, a method includes receiving, by processing circuitry,a signal indicative of a blood pressure of a patient and convolving, byprocessing circuitry, a kernel and blood pressure values of the signal.The method also includes determining, by processing circuitry, signalpeaks based on convolving the kernel and the blood pressure values ofthe signal. The method further includes determining, by the processingcircuitry, a set of mean arterial pressure values based on the bloodpressure values of the signal and the signal peaks. The method includesdetermining, by processing circuitry, an autoregulation status of thepatient based on the set of mean arterial pressure values.

The details of one or more examples are set forth in the accompanyingdrawings and the description below. Other features, objects, andadvantages will be apparent from the description and drawings, and fromthe claims.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a conceptual block diagram illustrating an example device.

FIG. 2 is a conceptual block diagram illustrating an example regionaloximetry device for monitoring the autoregulation status of a patient.

FIG. 3 illustrates an example graphical user interface includingautoregulation information presented on a display.

FIGS. 4, 5, 6, 7, and 8 are example graphs showing bypass and pulsatile(e.g., non-bypass) signals.

FIGS. 9A, 9B, 9C, and 9D are graphs illustrating example kernels.

FIGS. 10, 11, 12, 13, 14, 15, and 16 are flow diagrams illustratingexample techniques for determining a set of mean arterial pressurevalues, in accordance with some examples of this disclosure.

DETAILED DESCRIPTION

This disclosure describes devices, systems, and techniques fordetermining a set of mean arterial pressure values based on a signalindicative of a blood pressure of a patient. An arterial blood pressuresignal, which can be measured invasively or non-invasively, can beaffected by several types of conditions which may manifest as artifacts.These artifacts in the blood pressure signal may cause processingcircuitry of a system to determine that the blood pressure signal is notvalid. Examples of such conditions that may cause artifacts includearterial-line flushing, probe movements, and electrocautery. In otherexamples, a blood pressure monitoring device may not take into accountthese artifacts and may only display the current blood pressureinformation, even if the blood pressure information is contaminated bythese artifacts or other undesired noise. During clinical practice, aclinician may expect that the displayed blood pressure will beinaccurate during these times, for example, when the arterial line isbeing flushed. In such circumstances, the clinician may simply ignorethe readings taken from the blood pressure monitor and wait until areasonable figure comes back on the display. However, these lapses inaccurate blood pressures may prevent the clinician or a medical devicethat uses the blood pressure values from appropriately monitoring thepatient's blood pressure during these times.

Cardiac surgery patients often present extremely abnormal beatmorphologies that can present a challenge to the monitoring of meanarterial pressure. The monitoring of mean arterial pressure can bedifficult when presented with complex pulse morphologies. In addition,the determination of the autoregulation status may be based on thetrends of the arterial blood pressure signal, but some blood pressuredevices do not process artifacts that affect the trending informationthat is used for determining autoregulation status. In addition,existing blood pressure devices are not able to classify a portion ofthe blood pressure signal as pulsatile or as indicating acardiopulmonary bypass procedure.

The techniques of this disclosure may be able to identify, improve,correct, and/or exclude portions of a blood pressure signal that areaffected by conditions that cause artifacts such as line flushing,catheter adjustments, severe noise due to electrocautery, and/oralternating-current (AC) interference. In this manner, processingcircuitry may be configured to generate a filtered version of the bloodpressure signal to reduce the impact of these conditions to thedisplayed or otherwise utilized blood pressure signal. In some examples,the processing circuitry may be configured to continue determining meanarterial pressure values during a bypass procedure where there is nocardiac pulse in the blood pressure signal. Other physiologicalmonitoring devices without such bypass detecting techniques may not becapable of providing blood pressures during bypass periods. In contrast,a device of this disclosure may be used in the cardiovascular operatingroom and other applications and may be able to provide more accuratemean arterial pressure values for both bypass and non-bypass periods.

For example, processing circuitry of a blood pressure device may beconfigured to identify a portion of the signal including acharacteristic exceeding a threshold and determine the filtered signalby excluding or modifying the identified portion of the signal. Theprocessing circuitry can determine the set of mean arterial pressurevalues based on a filtered version of the signal.

Additionally or alternatively, the processing circuitry may beconfigured to identify a portion of the signal including acharacteristic indicating that the patient is undergoing acardiopulmonary bypass procedure. The processing circuitry may beconfigured to identify the portion at least in part by determining thatthe portion does not satisfy two or more non-bypass conditions. Inresponse to identifying the portion, the processing circuitry candetermine mean arterial pressure values for the identified portion basedon a moving average of the blood pressure values of the signal.

In some examples, the processing circuitry is configured to determinethe set of mean arterial pressure values by convolving a kernel with theblood pressure values of the signal. The processing circuitry may beconfigured to determine peaks and/or troughs of the signal based onconvolving the kernel and the blood pressure values. The processingcircuitry may be configured to convolve two or more kernels with theblood pressure values of the signal to produce two or more convolvedsignals. The processing circuitry can select the convolved signal withthe higher power level and use the kernel associated with the selectedsignal to determine peaks and/or troughs.

The processing circuitry is configured to use the mean arterial pressurevalues to determine an autoregulation status of the patient. By usingthe techniques of this disclosure, the processing circuitry maydetermine a more accurate, more robust, and more stable set of meanarterial pressure values. The determined set of mean arterial pressurevalues may be a more accurate representation of the patient state, ascompared to a set of mean arterial pressure values determined withoutthe techniques of this disclosure. The techniques of this disclosure maycreate a more robust set of mean arterial pressure values during eventssuch as motion noise, blood pressure artifacts such as line flushing,and bypass procedures.

The devices, systems, and techniques of this disclosure may allow forpresenting a more accurate indication of the autoregulation status ofthe patient. The presentation of more accurate and more stableinformation may result in increased confidence by a clinician viewingthe presented information, which may lead to more informed decisionmaking by the clinician. A clinician may lose confidence in theinformation presented by the processing circuitry if the information isless stable and/or less accurate. By determining a set of mean arterialpressure values using the techniques of this disclosure, the processingcircuitry may base the determination of autoregulation status on morestable and accurate mean arterial pressure values. By determining anautoregulation status using the techniques of this disclosure, theprocessing circuitry may reduce swings in the estimates of limits ofautoregulation caused by erroneous portions of a blood pressure signaland/or issues caused by cardiopulmonary bypass procedures.

The autoregulation status of a patient may be an indication that thecerebral autoregulation control mechanism of the patient is intact(e.g., functioning properly) or impaired (e.g., not functioningproperly). A cerebral autoregulation control mechanism of the body mayregulate cerebral blood flow (CBF) over a range of systemic bloodpressures. This range of systemic blood pressures may lie within a lowerlimit of autoregulation (LLA) and an upper limit of autoregulation(ULA). Outside of the LLA and the ULA, blood pressure directly drivesCBF, and cerebral autoregulation function may thus be consideredimpaired.

One method to determine the limits of autoregulation (e.g., the LLA andULA) noninvasively using near-infrared spectroscopy (NIRS) technologymay include the COx measure, which is a moving correlation index betweenmean arterial pressure (MAP) and regional oxygen saturation (rSO₂). TheCOx measure (e.g., using the Pearson correlation coefficient) is derivedfrom the correlation between rSO₂ and MAP. COx relates to the regressionline fit or linear correlation between rSO₂ and MAP over a time windowhaving a particular length, such as three hundred seconds, in someexamples. The COx method may be used to produce a representation of apatient's blood-pressure-dependent autoregulation status.

When the cerebral autoregulation is intact for a patient, there istypically no, or little, correlation between MAP and rSO₂. In contrast,MAP and rSO₂ typically directly correlate (e.g., the COx value isapproximately positive one) when the cerebral autoregulation isimpaired. In practice, however, sensed data indicative of autoregulationmay be noisy and/or there might be a slightly correlated relationshipbetween variables (e.g., MAP and rSO₂) even when cerebral autoregulationis intact for the patient.

Some existing systems for monitoring autoregulation may determine apatient's autoregulation status based on various physiological parametervalues (also referred to herein as physiological values), such asarterial blood pressure and oxygen saturation. Measurement of suchphysiological values may be subject to various sources of error, such asnoise caused by relative sensor and patient motion, operator error, poorquality measurements, drugs, or other anomalies. However, some existingsystems for monitoring autoregulation may not reduce the various sourcesof error when utilizing the measured physiological values to determinethe patient's autoregulation status. Furthermore, some existing systemsmay not determine and/or utilize a reliable metric to determine whetherthe autoregulation status calculated from the physiological values isreliable. Accordingly, the autoregulation status determined by suchexisting systems may be less accurate or less reliable.

In an intact region of cerebral autoregulation, there may be nocorrelation between these variables whereas in an impaired region ofcerebral autoregulation, the correlation index should approximate unity.In practice, however, the data may be noisy and/or the intact region mayexhibit a slightly positive relationship. This positive relationship mayrender traditional autoregulation limit calculations difficult toperform, resulting in the need for manual interpretation of the datausing arbitrary thresholds. Further, the underlying mathematics of thetechnique may be asymmetric in terms of the results produced forimpaired and intact regions and may be, in fact, not computable for theideal case within the intact region.

A physician may monitor a patient's autoregulation through the use ofvarious monitoring devices and systems that measure variousphysiological parameters. In certain aspects of the present disclosure,a patient's autoregulation may be monitored by correlating measurementsof the patient's blood pressure (e.g., arterial blood pressure) withmeasurements of the patient's oxygen saturation (e.g., regional oxygensaturation). In particular, a COx value may be derived based at least inpart on a linear correlation between the patient's blood pressure andoxygen saturation. In addition, in certain aspects of the presentdisclosure, the patient's autoregulation may be monitored by correlatingmeasurements of the patient's blood pressure with measurements of thepatient's blood volume (e.g., blood volume proxy). In particular, ahemoglobin volume index (HVx) may be derived based at least in part on alinear correlation between the patient's blood pressure and bloodvolume.

While features of the present disclosure are discussed with reference toCOx, in other examples, various other linear correlations such as HVxmay be determined to help evaluate a patient's autoregulation status.For example, a linear correlation between measurements of a patient'sblood pressure and measurements of a patient's cerebral blood flow mayderive a mean velocity index (Mx). As a further example, a linearcorrelation between measurements of a patient's blood pressure andmeasurements of a patient's intracranial pressure may derive a pressurereactivity index (PRx). In certain situations, these indices may beutilized to determine or help evaluate a patient's autoregulation. Thedevices, systems, and techniques of this disclosure can also be appliedto the determination of indices such as HVx, Mx, PRx, and/or any otherindices, coefficients, and correlations. For example, processingcircuitry may be configured to determine an estimate of a limit ofautoregulation based on a set of HVx indices, a set of Mx indices,and/or a set of PRx indices.

Additional example details of the parameters that can be used fordetermining a limit of autoregulation may be found in commonly assignedU.S. Patent Application Publication No. 2016/0367197 filed on Jun. 16,2016, entitled “Systems and Methods for Reducing Signal Noise WhenMonitoring Autoregulation,” and commonly assigned U.S. PatentApplication Publication No. 2017/0105631 filed on Oct. 18, 2016,entitled “System and Method for Providing Blood Pressure Safe ZoneIndication During Autoregulation Monitoring,” which are incorporatedherein by reference in their entirety. A gradients-based method can alsobe used to determine an estimate of a limit of autoregulation.Processing circuitry can perform a gradients-based method by analyzing arelationship between a change in the patient's blood pressure and achange in the patient's oxygen saturation over a period of time.Additional example details of gradients-based methods are described incommonly assigned U.S. Patent Application Publication No. 2018/0014791filed Jul. 13, 2017, and entitled “Systems and Methods of MonitoringAutoregulation,” the entire content of which is incorporated herein byreference.

FIG. 1 is a conceptual block diagram illustrating an example device 100.

Device 100 includes processing circuitry 110, memory 120, user interface130, display 132, sensing circuitry 140-142, and sensing device(s)150-152. In some examples, device 100 may be configured to determine anddisplay the cerebral autoregulation status of a patient, e.g., during amedical procedure or for more long-term monitoring, such as fetalmonitoring. A clinician may receive information regarding the cerebralautoregulation status of a patient via display 132 and adjust treatmentor therapy to the patient based on the cerebral autoregulation statusinformation.

Device 100 can directly sense the blood pressure of a patient.Additionally, or alternatively, device 100 may receive blood pressuredata from a blood pressure device that senses the blood pressure of thepatient. Device 100 may directly connect to the blood pressure deviceand/or a regional oximetry device via wired or wireless communication orindirectly receive data via one or more networks. In this manner, device100 may be similar to a multi-parametric monitor that receives data frommultiple devices. For example, device 100 can directly or indirectlyreceive data and/or signals from sensing device 150, 151, and/or 152,which may be a part of device 100 or may be separate devices. Device 100can incorporate all, some, or none of sensing circuitry 140-142 andsensing devices 150-152. Some or all of sensing circuitry 140-142 andsensing devices 150-152 may be located outside of device 100 in separatedevices. Device 100 can incorporate all of the data and signals receivedfrom sensing devices 150-152 to display autoregulation information.

Processing circuitry 110, as well as other processors, processingcircuitry, controllers, control circuitry, and the like, describedherein, may include one or more processors. Processing circuitry 110 mayinclude any combination of integrated circuitry, discrete logiccircuitry, analog circuitry, such as one or more microprocessors,digital signal processors (DSPs), application specific integratedcircuits (ASICs), or field-programmable gate arrays (FPGAs). In someexamples, processing circuitry 110 may include multiple components, suchas any combination of one or more microprocessors, one or more DSPs, oneor more ASICs, or one or more FPGAs, as well as other discrete orintegrated logic circuitry, and/or analog circuitry.

Memory 120 may be configured to store measurements of physiologicalparameters, MAP values, rSO₂ values, COx values, and value(s) of an LLAand/or a ULA, for example. Memory 120 may also be configured to storedata such as blood pressure values, mean arterial pressure values,thresholds, blood pressure variation values, threshold rates, non-bypassconditions, and kernels. The blood pressure values, mean arterialpressure values, thresholds, blood pressure variation values, thresholdrates, non-bypass conditions, and/or kernels may stay constantthroughout the use of device 100 and across multiple patients, or thesevalues may change over time.

In some examples, memory 120 may store program instructions, which mayinclude one or more program modules, which are executable by processingcircuitry 110. When executed by processing circuitry 110, such programinstructions may cause processing circuitry 110 to provide thefunctionality ascribed to it herein. The program instructions may beembodied in software, firmware, and/or RAMware. Memory 120 may includeany volatile, non-volatile, magnetic, optical, or electrical media, suchas a random access memory (RAM), read-only memory (ROM), non-volatileRAM (NVRAM), electrically-erasable programmable ROM (EEPROM), flashmemory, or any other digital media.

User interface 130 and/or display 132 may be configured to presentinformation to a user (e.g., a clinician). User interface 130 and/ordisplay 132 may be configured to present a graphical user interface to auser, where each graphical user interface may include indications ofvalues of one or more physiological parameters of a patient. Forexample, processing circuitry 110 may be configured to present bloodpressure values, physiological parameter values, and indications ofautoregulation status (e.g., cerebral autoregulation status) of apatient via display 132. In some examples, if processing circuitry 110determines that the autoregulation status of the patient is impaired,then processing circuitry 110 may present a notification (e.g., analert) indicating the impaired cerebral autoregulation status viadisplay 132. As another example, processing circuitry 110 may present,via display 132, estimates of rSO₂ for a patient, an estimate of theblood oxygen saturation (SpO₂) determined by processing circuitry 110,pulse rate information, respiration rate information, blood pressure,any other patient parameters, or any combination thereof. Processingcircuitry 110 may also be configured to present, via display 132, anindication of whether a signal indicates that the patient is, or waspreviously, undergoing a cardiopulmonary bypass procedure.

User interface 130 and/or display 132 may include a monitor, cathode raytube display, a flat panel display such as a liquid crystal (LCD)display, a plasma display, a light emitting diode (LED) display, and/orany other suitable display. User interface 130 and/or display 132 may bepart of a personal digital assistant, mobile phone, tablet computer,laptop computer, any other suitable computing device, or any combinationthereof, with a built-in display or a separate display. User interface130 may also include means for projecting audio to a user, such asspeaker(s). Processing circuitry 110 may be configured to present, viauser interface 130, a visual, audible, tactile, or somatosensorynotification (e.g., an alarm signal) indicative of the patient'sautoregulation status and/or a notification indicative of the patient'slimit(s) of autoregulation.

User interface 130 may include or be part of any suitable device forconveying such information, including a computer workstation, a server,a desktop, a notebook, a laptop, a handheld computer, a mobile device,or the like. In some examples, processing circuitry 110 and userinterface 130 may be part of the same device or supported within onehousing (e.g., a computer or monitor). In other examples, processingcircuitry 110 and user interface 130 may be separate devices configuredto communicate through a wired connection or a wireless connection(e.g., communication interface 290 shown in FIG. 2).

Sensing circuitry 140-142 may be configured to receive physiologicalsignals sensed by respective sensing device(s) 150-152 and communicatethe physiological signals to processing circuitry 110. Sensing devices150-152 and sensing circuitry 140-142 can deliver the physiologicalsignals directly to processing circuitry 110 or sensing circuitry140-142 can modify the physiological signals (e.g., throughpre-processing) before delivering signals to processing circuitry 110.Sensing device(s) 150-152 may include any sensing hardware configured tosense a physiological parameter of a patient, such as, but not limitedto, one or more electrodes, optical receivers, blood pressure cuffs, orthe like. Sensing circuitry 140-142 may convert the physiologicalsignals to usable signals for processing circuitry 110, such thatprocessing circuitry 110 is configured to receive signals generated bysensing circuitry 140-142. Sensing circuitry 140-142 may receive signalsindicating physiological parameters from a patient, such as, but notlimited to, blood pressure, regional oxygen saturation, blood volume,heart rate, and respiration. Sensing circuitry 140-142 may include, butare not limited to, blood pressure sensing circuitry, oxygen saturationsensing circuitry, blood volume sensing circuitry, heart rate sensingcircuitry, temperature sensing circuitry, electrocardiography (ECG)sensing circuitry, electroencephalogram (EEG) sensing circuitry, or anycombination thereof. In some examples, sensing circuitry 140-142 and/orprocessing circuitry 110 may include signal processing circuitry such asan analog-to-digital converter.

In some examples, oxygen saturation sensing device 150 is a regionaloxygen saturation sensor configured to generate an oxygen saturationsignal indicative of blood oxygen saturation within the venous,arterial, and/or capillary systems within a region of the patient. Forexample, oxygen saturation sensing device 150 may be configured to beplaced on the patient's forehead and may be used to determine the oxygensaturation of the patient's blood within the venous, arterial, and/orcapillary systems of a region underlying the patient's forehead (e.g.,in the cerebral cortex).

In such cases, oxygen saturation sensing device 150 may include emitter160 and detector 162. Emitter 160 may include at least two lightemitting diodes (LEDs), each configured to emit at different wavelengthsof light, e.g., red or near infrared light. In some examples, lightdrive circuitry (e.g., within sensing device 150, sensing circuitry 140,and/or processing circuitry 110) may provide a light drive signal todrive emitter 160 and to cause emitter 160 to emit light. In someexamples, the LEDs of emitter 160 emit light in the wavelength range ofabout 600 nanometers (nm) to about 1,000 nm. In a particular example,one LED of emitter 160 is configured to emit light at a wavelength ofabout 730 nm and the other LED of emitter 160 is configured to emitlight at a wavelength of about 810 nm. Other wavelengths of light mayalso be used in other examples.

Detector 162 may include a first detection element positioned relatively“close” (e.g., proximal) to emitter 160 and a second detection elementpositioned relatively “far” (e.g., distal) from emitter 160. Lightintensity of multiple wavelengths may be received at both the “close”and the “far” detector 162. For example, if two wavelengths are used,the two wavelengths may be contrasted at each location and the resultingsignals may be contrasted to arrive at a regional saturation value thatpertains to additional tissue through which the light received at the“far” detector passed (tissue in addition to the tissue through whichthe light received by the “close” detector passed, e.g., the braintissue), when it was transmitted through a region of a patient (e.g., apatient's cranium). Surface data from the skin and skull may besubtracted out, to generate a regional oxygen saturation signal for thetarget tissues over time. Oxygen saturation sensing device 150 mayprovide the regional oxygen saturation signal to processing circuitry110 or to any other suitable processing device to enable evaluation ofthe patient's autoregulation status.

In operation, blood pressure sensing device 151 and oxygen saturationsensing device 150 may each be placed on the same or different parts ofthe patient's body. For example, blood pressure sensing device 151 andoxygen saturation sensing device 150 may be physically separate fromeach other and may be separately placed on the patient. As anotherexample, blood pressure sensing device 151 and oxygen saturation sensingdevice 150 may in some cases be part of the same sensor or supported bya single sensor housing. For example, blood pressure sensing device 151and oxygen saturation sensing device 150 may be part of an integratedoximetry system configured to non-invasively measure blood pressure(e.g., based on time delays in a PPG signal) and regional oxygensaturation. One or both of blood pressure sensing device 151 or oxygensaturation sensing device 150 may be further configured to measure otherparameters, such as hemoglobin, respiratory rate, respiratory effort,heart rate, saturation pattern detection, response to stimulus such asbispectral index (BIS) or electromyography (EMG) response to electricalstimulus, or the like. While an example device 100 is shown in FIG. 1,the components illustrated in FIG. 1 are not intended to be limiting.Additional or alternative components and/or implementations may be usedin other examples.

Blood pressure sensing device 151 may be any sensor or device configuredto obtain the patient's blood pressure (e.g., arterial blood pressure).For example, blood pressure sensing device 151 may include a bloodpressure cuff for non-invasively monitoring blood pressure or anarterial line for invasively monitoring blood pressure. In certainexamples, blood pressure sensing device 151 may include one or morepulse oximetry sensors. In some such cases, the patient's blood pressuremay be derived by processing time delays between two or morecharacteristic points within a single plethysmography (PPG) signalobtained from a single pulse oximetry sensor. In some examples, sensingdevices 150, 151, and/or 152 deliver data to a multi-parametric monitorthat can sense blood pressure and/or oxygen saturation of a patient. Atablet or other computing device can also be configured to receive dataand/or signals from sensing devices 150, 151, and/or 152, where the dataand/or signals indicate blood pressure and/or oxygen saturation of thepatient.

Additional example details of deriving blood pressure based on acomparison of time delays between certain components of a single PPGsignal obtained from a single pulse oximetry sensor are described incommonly assigned U.S. Patent Application Publication No. 2009/0326386filed Sep. 30, 2008, and entitled “Systems and Methods for Non-InvasiveBlood Pressure Monitoring,” the entire content of which is incorporatedherein by reference. In other cases, the patient's blood pressure may becontinuously, non-invasively monitored via multiple pulse oximetrysensors placed at multiple locations on the patient's body. As describedin commonly assigned U.S. Pat. No. 6,599,251, entitled “ContinuousNon-invasive Blood Pressure Monitoring Method and Apparatus,” the entirecontent of which is incorporated herein by reference, multiple PPGsignals may be obtained from the multiple pulse oximetry sensors, andthe PPG signals may be compared against one another to estimate thepatient's blood pressure. Regardless of its form, blood pressure sensingdevice 151 may be configured to generate a blood pressure signalindicative of the patient's blood pressure (e.g., arterial bloodpressure) over time. Blood pressure sensing device 151 may provide theblood pressure signal to sensing circuitry 141, processing circuitry110, or to any other suitable processing device to enable evaluation ofthe patient's autoregulation status.

Sensing circuitry 141 may be configured to generate a signal indicativeof a blood pressure of a patient based on a physiological signal sensedby sensing device 151. Processing circuitry 110 may be configured totimestamp the received signal to account for possible missing periods ofsignal and to allow the possibility to synchronize the processed signalwith other parameters (e.g., signals received from sensing circuitry 140or 142). In some examples, the sampling frequency for the signalreceived from sensing circuitry 141 is one hundred hertz, although othersampling frequencies are possible. Based on the received signal,processing circuitry 110 may be configured to determine mean arterialpressure values at an output rate of one hertz, although other outputrates for mean arterial pressure values are possible. The signalreceived by processing circuitry 110 from sensing circuitry 141 may bereferred as a raw signal, an unfiltered signal, and/or a blood pressuresignal. In other examples, processing circuitry 110 may directly receivea signal (e.g., analog or digital signal) from an external (such asblood pressure sensing device 151) that is indicative of the bloodpressure without further processing and/or sensing circuitry 141. Inother examples, processing circuitry 110 may perform some or all of theprocesses attributable to sensing circuitry 141 described herein.

Processing circuitry 110 may be configured to determine mean arterialpressure values based on a sampling window of, e.g., five or ten secondsfor the blood pressure values in the signal received from sensingcircuitry 141. Processing circuitry 110 may also be configured todetermine systolic pressure, diastolic pressure, and heart rate signalsusing the sampling window. Processing circuitry 110 may be configured toassociate one or more flags with each portion of the signal to identifythe portion as bypass, to identify the event of a possible artifact, toidentify a pulsatile signal, or identify a disconnection of sensingdevice 151 from the patient. For example, processing circuitry 110 maybe configured to identify a portion of the signal including acharacteristic that exceeds a threshold and to set a flag for theidentified portion. Processing circuitry 110 may be configured todetermine beat-to-beat signal information, containing a time stampassociated with each beat, systolic pressure, diastolic pressure, andbeat duration. Processing circuitry 110 can integrate the arterial bloodpressure over the cardiac cycle to provide a more accurate measure.Alternatively, processing circuitry 110 can calculate a mean arterialpressure value as the average value over a fixed moving window, whichcan be useful in anomalous conditions (e.g., arrhythmia, unusual beatshapes, and/or heart problems), when beat-to-beat pulse identificationis problematic or not possible.

From a high-level perspective, processing circuitry 110 can determinemean arterial pressure values using three steps, as shown in FIG. 11.First, processing circuitry 110 can clean or filter the arterial bloodpressure signal received from sensing circuitry 141 by identifyingartifacts in the raw signal. Depending on the artifacts andcharacteristics of a portion of the signal, processing circuitry 110 maybe configured to exclude or modify the portion of the signal during thecleaning or filtering process. By excluding or modifying one or moreportions of the signal, processing circuitry 110 can generate a filteredsignal.

Second, processing circuitry 110 may be configured to identify a portionof the filtered signal as pulsatile or as indicating that the patient isundergoing a cardiopulmonary bypass procedure. Processing circuitry 110can identify a portion of the filtered signal as pulsatile or bypassbased on characteristics such as the power of the filtered signal, adiastolic value, a prediction value, and one or more non-bypassconditions. Processing circuitry 110 can use a portion of a filteredsignal or a portion of an unfiltered signal to determine whether theportion is pulsatile or indicates that the patient is undergoing acardiopulmonary bypass procedure.

Third, processing circuitry 110 may be configured to determine a set ofmean arterial pressure values based on the filtered signal. In examplesin which processing circuitry 110 identifies a portion of the signal aspulsatile, processing circuitry 110 may be configured to determine meanarterial pressure values using beat-to-beat information. Processingcircuitry 110 can determine beat-to-beat information at least in part byconvolving a kernel and the blood pressure values of the signal anddetermining signal peaks or troughs based on the convolved signal. Inexamples in which processing circuitry 110 identifies a portion of thesignal as indicating a bypass procedure, processing circuitry 110 may beconfigured to determine mean arterial pressure values using a movingaverage of the blood pressure signal.

Processing circuitry 110 may be configured to determine a set ofcorrelation coefficient values based on the set of mean arterialpressure values and a set of oxygen saturation values. Processingcircuitry 110 can determine an estimate of a limit of autoregulation ofa patient based on the set of correlation coefficient values. Thecorrelation coefficient values may be near positive one at very lowvalues and very high blood pressure values. Therefore, to determine anestimate of the lower limit of autoregulation, processing circuitry 110may determine the lowest blood pressure value at which the associatedcorrelation coefficient values are below a threshold level, such as 0.8,0.7, 0.6, 0.5, 0.4, 0.3, 0.2, 0.1, or 0.0, where full correlation isapproximately 1.0 and no correlation is approximately 0.0. To determinean estimate of the upper limit of autoregulation, processing circuitry110 may determine the highest blood pressure value at which theassociated correlation coefficient values are below a threshold level.

Processing circuitry 110 is also configured to determine anautoregulation status of the patient based on the mean arterial pressurevalues. For example, processing circuitry 110 may determine whether thecurrent mean arterial pressure value of the patient is greater than theestimate of the lower limit of autoregulation. If the current meanarterial pressure value is greater than the estimate of the lower limitof autoregulation, then processing circuitry 110 can determine that thepatient has intact autoregulation, unless the current mean arterialpressure value is greater than the upper limit of autoregulation of thepatient.

Processing circuitry 110 may be configured to output, for display viadisplay 132 of user interface 130, an indication of the autoregulationstatus. To present an indication of autoregulation status, display 132may present a graphical user interface such as graphical user interface300 shown in FIG. 3. As described in further detail below, graphicaluser interface 300 includes an indicator of autoregulation status 350.The indication of autoregulation status may include text, colors, and/oraudio presented to a user. Processing circuitry 110 may be furtherconfigured to present an indication of one or more limits ofautoregulation (e.g., indicators 360 and 370).

By determining a set of mean arterial pressure values using thetechniques of this disclosure, processing circuitry 110 can determinemore accurate and more stable mean arterial pressure values, as well asmore accurate determinations of correlation and co-trending with otherparameters. By using more accurate mean arterial pressure values,processing circuitry 110 can make more accurate determinations of theautoregulation status of the patient.

The techniques of this disclosure are described with respect toprocessing circuitry 110 of device 100. However, in some examples, someor all of the functionality attributed to processing circuitry 110 maybe performed by processing circuitry in a separate blood pressuresensing device that includes sensing circuitry 141 and/or sensing device151. The blood pressure sensing device may be an external device thatdelivers data and/or signals to a multi-parametric monitor. The externaldevice can be coupled to processing circuitry 110 of device 100.

Although other example devices, systems, and techniques are possible,device 100 may be configured to determine an autoregulation status of apatient based on correlation coefficient values derived from meanarterial pressure values and oxygen saturation values. Alternatively,processing circuitry 110 may determine the first estimate of the limitof autoregulation based on HVx values, BVS values, and/or oxygensaturation values. Regional oximetry device 200 of FIG. 2 includesadditional detail on how processing circuitry 110 can determine oxygensaturation values based on a physiological signal received from sensingdevice 150.

FIG. 2 is a conceptual block diagram illustrating an example regionaloximetry device 200 for monitoring the autoregulation status of apatient. In the example shown in FIG. 2, regional oximetry device 200 iscoupled to sensing device 250 and may be collectively referred to as aregional oximetry system, which each generate and process physiologicalsignals of a subject. In some examples, sensing device 250 and regionaloximetry device 200 may be part of an oximeter. As shown in FIG. 2,regional oximetry device 200 includes back-end processing circuitry 214,user interface 230, light drive circuitry 240, front-end processingcircuitry 216, control circuitry 245, and communication interface 290.Regional oximetry device 200 may be communicatively coupled to sensingdevice 250. Regional oximetry device 200 is an example of device 100shown in FIG. 1.

In some examples, regional oximetry device 200 may also include a bloodpressure sensor and/or a blood volume sensor (e.g., sensing devices 151and 152 shown in FIG. 1). The blood pressure sensor may be an externalblood pressure device that can deliver data and/or signals to a regionaloximetry device or a multi-parametric monitor. The blood pressure sensorcan also be a part of device 200, as shown in FIG. 1 with respect todevice 100. In some examples, sensing device 250 may be a separatedevice that is external to regional oximetry device 200, rather than adevice that is part of regional oximetry device 200.

In the example shown in FIG. 2, sensing device 250 includes light source260, detector 262, and detector 263. In some examples, sensing device250 may include more than two detectors. Light source 260 may beconfigured to emit photonic signals having two or more wavelengths oflight (e.g., red and infrared (IR)) into a subject's tissue. Forexample, light source 260 may include a red light emitting light sourceand an IR light emitting light source, (e.g., red and IR light emittingdiodes (LEDs)), for emitting light into the tissue of a subject togenerate physiological signals. In some examples, the red wavelength maybe between about 600 nm and about 700 nm, and the IR wavelength may bebetween about 800 nm and about 1,000 nm. Other wavelengths of light maybe used in other examples. Light source 260 may include any number oflight sources with any suitable characteristics. In examples in which anarray of sensors is used in place of sensing device 250, each sensingdevice may be configured to emit a single wavelength. For example, afirst sensing device may emit only a red light while a second sensingdevice may emit only an IR light. In some examples, light source 260 maybe configured to emit two or more wavelengths of near-infrared light(e.g., wavelengths between 600 nm and 1,000 nm) into a subject's tissue.In some examples, light source 260 may be configured to emit fourwavelengths of light (e.g., 724 nm, 770 nm, 810 nm, and 850 nm) into asubject's tissue. In some examples, the subject may be a medicalpatient.

As used herein, the term “light” may refer to energy produced byradiative sources and may include one or more of ultrasound, radio,microwave, millimeter wave, infrared, visible, ultraviolet, gamma ray orX-ray electromagnetic radiation. Light may also include any wavelengthwithin the radio, microwave, infrared, visible, ultraviolet, or X-rayspectra, and that any suitable wavelength of electromagnetic radiationmay be appropriate for use with the present techniques. Detectors 262and 263 may be chosen to be specifically sensitive to the chosentargeted energy spectrum of light source 260.

In some examples, detectors 262 and 263 may be configured to detect theintensity of multiple wavelengths of near-infrared light. In someexamples, detectors 262 and 263 may be configured to detect theintensity of light at the red and IR wavelengths. In some examples, anarray of detectors may be used and each detector in the array may beconfigured to detect an intensity of a single wavelength. In operation,light may enter detector 262 after passing through the subject's tissue,including skin, bone, and other shallow tissue (e.g., non-cerebraltissue and shallow cerebral tissue). Light may enter detector 263 afterpassing through the subject's tissue, including skin, bone, othershallow tissue (e.g., non-cerebral tissue and shallow cerebral tissue),and deep tissue (e.g., deep cerebral tissue). Detectors 262 and 263 mayconvert the intensity of the received light into an electrical signal.The light intensity may be directly related to the absorbance and/orreflectance of light in the tissue. That is, when more light at acertain wavelength is absorbed or reflected, less light of thatwavelength is received from the tissue by detectors 262 and 263.

After converting the received light to an electrical signal, detectors262 and 263 may send the detection signals to regional oximetry device200, where the detection signals may be processed and physiologicalparameters may be determined (e.g., based on the absorption of the redand IR wavelengths in the subject's tissue at both detectors). In someexamples, one or more of the detection signals may be preprocessed bysensing device 250 before being transmitted to regional oximetry device200. Additional example details of determining oxygen saturation basedon light signals may be found in commonly assigned U.S. Pat. No.9,861,317, which issued on Jan. 9, 2018, and is entitled “Methods andSystems for Determining Regional Blood Oxygen Saturation,” the entirecontent of which is incorporated herein by reference.

Control circuitry 245 may be coupled to light drive circuitry 240,front-end processing circuitry 216, and back-end processing circuitry214, and may be configured to control the operation of these components.In some examples, control circuitry 245 may be configured to providetiming control signals to coordinate their operation. For example, lightdrive circuitry 240 may generate one or more light drive signals, whichmay be used to turn on and off light source 260, based on the timingcontrol signals provided by control circuitry 245. Front-end processingcircuitry 216 may use the timing control signals to operatesynchronously with light drive circuitry 240. For example, front-endprocessing circuitry 216 may synchronize the operation of ananalog-to-digital converter and a demultiplexer with the light drivesignal based on the timing control signals. In addition, the back-endprocessing circuitry 214 may use the timing control signals tocoordinate its operation with front-end processing circuitry 216.

Light drive circuitry 240, as discussed above, may be configured togenerate a light drive signal that is provided to light source 260 ofsensing device 250. The light drive signal may, for example, control theintensity of light source 260 and the timing of when light source 260 isturned on and off. In some examples, light drive circuitry 240 providesone or more light drive signals to light source 260. Where light source260 is configured to emit two or more wavelengths of light, the lightdrive signal may be configured to control the operation of eachwavelength of light. The light drive signal may comprise a single signalor may comprise multiple signals (e.g., one signal for each wavelengthof light).

Front-end processing circuitry 216 may perform any suitable analogconditioning of the detector signals. The conditioning performed mayinclude any type of filtering (e.g., low pass, high pass, band pass,notch, or any other suitable filtering), amplifying, performing anoperation on the received signal (e.g., taking a derivative, averaging),performing any other suitable signal conditioning (e.g., converting acurrent signal to a voltage signal), or any combination thereof. Theconditioned analog signals may be processed by an analog-to-digitalconverter of circuitry 216, which may convert the conditioned analogsignals into digital signals. Front-end processing circuitry 216 mayoperate on the analog or digital form of the detector signals toseparate out different components of the signals. Front-end processingcircuitry 216 may also perform any suitable digital conditioning of thedetector signals, such as low pass, high pass, band pass, notch,averaging, or any other suitable filtering, amplifying, performing anoperation on the signal, performing any other suitable digitalconditioning, or any combination thereof. Front-end processing circuitry216 may decrease the number of samples in the digital detector signals.In some examples, front-end processing circuitry 216 may also removedark or ambient contributions to the received signal.

Back-end processing circuitry 214 may include processing circuitry 210and memory 220. Processing circuitry 210 may include an assembly ofanalog or digital electronic components and may be configured to executesoftware, which may include an operating system and one or moreapplications, as part of performing the functions described herein withrespect to, e.g., processing circuitry 110. Processing circuitry 210 mayreceive and further process one or more signals received from front-endprocessing circuitry 216. For example, processing circuitry 210 maydetermine physiological parameter values based on the received signals.For example, processing circuitry 210 may compute one or more ofregional oxygen saturation, blood oxygen saturation (e.g., arterial,venous, or both), pulse rate, respiration rate, respiration effort,blood pressure, hemoglobin concentration (e.g., oxygenated,deoxygenated, and/or total), any other suitable physiologicalparameters, or any combination thereof.

Processing circuitry 210 may perform any suitable signal processing of asignal, such as any suitable band-pass filtering, adaptive filtering,closed-loop filtering, any other suitable filtering, and/or anycombination thereof. Processing circuitry 210 may also receive inputsignals from additional sources not shown. For example, processingcircuitry 210 may receive an input signal containing information abouttreatments provided to the subject from user interface 230. Additionalinput signals may be used by processing circuitry 210 in any of thedeterminations or operations it performs in accordance with back-endprocessing circuitry 214 or regional oximetry device 200.

Processing circuitry 210 is an example of processing circuitry 110 andis configured to perform the techniques of this disclosure. For example,processing circuitry 210 is configured to receive signals indicative ofphysiological parameters including a blood pressure of the patient. Forexample, processing circuitry 210 can identify a first portion of asignal indicating blood pressure of the patient, where the first portionincludes a first characteristic that exceeds a first threshold.Processing circuitry 210 may also identify a second portion of thesignal including a second characteristic that exceeds a secondthreshold. Processing circuitry 210 may then determine a filtered signalby excluding or modifying the first portion and the second portion.Processing circuitry 210 may determine a set of mean arterial pressurevalues based on the filtered signal.

Processing circuitry 210 can determine the autoregulation status of thepatient based on the set of mean arterial pressure values. For example,processing circuitry 210 is also configured to determine values ofphysiological parameters based on the signals and determine correlationcoefficient values based on the values of the physiological parameters.Processing circuitry 210 can determine correlation coefficients based onvalues of mean arterial pressure and values of oxygen saturation.Processing circuitry 210 may determine a limit of autoregulation basedon a set of correlation coefficient values. To determine theautoregulation status of the patient, processing circuitry 210 cancompare the current mean arterial pressure value to the limit ofautoregulation.

Memory 220 may include any suitable computer-readable media capable ofstoring information that can be interpreted by processing circuitry 210.In some examples, memory 220 may store measurements of physiologicalparameters, mean arterial pressure values, oxygen saturation values,correlation coefficient values, threshold rates, threshold values,threshold time durations, blood pressure variation values, predeterminedranges of variation, maximum and minimum blood pressure values,threshold rates, non-bypass conditions, and kernels, any otherdetermined values, or any combination thereof, in a memory device forlater retrieval. Back-end processing circuitry 214 may becommunicatively coupled with user interface 230 and communicationinterface 290.

User interface 230 may include input device 234, display 232, andspeaker 236. User interface 230 is an example of user interface 130shown in FIG. 1, and display 232 is an example of display 132 shown inFIG. 1. User interface 230 may include, for example, any suitable devicesuch as one or more medical devices (e.g., a medical monitor thatdisplays various physiological parameters, a medical alarm, or any othersuitable medical device that either displays physiological parameters oruses the output of back-end processing 214 as an input), one or moredisplay devices (e.g., monitor, personal digital assistant (PDA), mobilephone, tablet computer, clinician workstation, any other suitabledisplay device, or any combination thereof), one or more audio devices,one or more memory devices, one or more printing devices, any othersuitable output device, or any combination thereof.

Input device 234 may include any type of user input device such as akeyboard, a mouse, a touch screen, buttons, switches, a microphone, ajoy-stick, a touch pad, or any other suitable input device orcombination of input devices. In other examples, input device 234 may bea pressure-sensitive or presence-sensitive display that is included aspart of display 232. Input device 234 may also receive inputs to selecta model number of sensing device 250, blood pressure sensor 250 (FIG.2), or blood pressure processing equipment. In some examples, processingcircuitry 210 may determine a threshold rate and/or a length of a windowof time based on user input received from input device 234.

In some examples, the subject may be a medical patient and display 232may exhibit a list of values which may generally apply to the subject,such as, for example, an oxygen saturation signal indicator, a bloodpressure signal indicator, a COx signal indicator, a COx valueindicator, and/or an autoregulation status indicator. Display 232 mayalso be configured to present additional physiological parameterinformation. Graphical user interface 300 shown in FIG. 3 is an exampleof an interface that can be presented via display 232 of FIG. 2.Additionally, display 232 may present, for example, one or moreestimates of a subject's regional oxygen saturation generated byregional oximetry device 200 (referred to as an “rSO₂” measurement).Display 232 may also present indications of the upper and lower limitsof autoregulation. Speaker 236 within user interface 230 may provide anaudible sound that may be used in various examples, such as for example,sounding an audible alarm in the event that the autoregulation status ofa patient is impaired or that the patient's physiological parameters arenot within a predefined normal range.

Communication interface 290 may enable regional oximetry device 200 toexchange information with external devices. Communication interface 290may include any suitable hardware, software, or both, which may allowregional oximetry device 200 to communicate with electronic circuitry, adevice, a network, a server or other workstations, a display, or anycombination thereof. For example, regional oximetry device 200 mayreceive MAP values and/or oxygen saturation values from an externaldevice via communication interface 290.

The components of regional oximetry device 200 that are shown anddescribed as separate components are shown and described as such forillustrative purposes only. In some examples the functionality of someof the components may be combined in a single component. For example,the functionality of front-end processing circuitry 216 and back-endprocessing circuitry 214 may be combined in a single processor system.Additionally, in some examples the functionality of some of thecomponents of regional oximetry device 200 shown and described hereinmay be divided over multiple components. For example, some or all of thefunctionality of control circuitry 245 may be performed in front-endprocessing circuitry 216, in back-end processing circuitry 214, or both.In other examples, the functionality of one or more of the componentsmay be performed in a different order or may not be required. In someexamples, all of the components of regional oximetry device 200 can berealized in processor circuitry.

FIG. 3 illustrates an example graphical user interface 300 includingautoregulation information presented on a display. FIG. 3 is an exampleof a presentation by processing circuitry 110 on display 132 shown inFIG. 1 or by processing circuitry 210 on display 232 shown in FIG. 2.Graphical user interface 300 may be configured to display variousinformation related to blood pressure, oxygen saturation, the COx index,limits of autoregulation, and/or autoregulation status. As shown,graphical user interface 300 may include oxygen saturation signalindicator 310, blood pressure signal indicator 320, and COx signalindicator 330. Graphical user interface 300 may include COx valueindicator 340, autoregulation status indicator 350, and limit ofautoregulation indicators 360 and 370.

Blood pressure signal indicator 320 may present a set of MAP valuesdetermined by processing circuitry 110 of device 100. Processingcircuitry 110 can use the set of MAP values determined from a filteredsignal for presenting as blood pressure signal indicator 320. However,processing circuitry 110 may be configured to use a different set ofdata for presenting as blood pressure signal 320. In some examples,processing circuitry 110 uses the set of MAP values determined from thefiltered signal only for determining correlation coefficient values.

For example, processing circuitry 110 can identify portions of the bloodpressure signal with characteristics that exceed a threshold. Processingcircuitry 110 can exclude or modify the identified portions to create afiltered signal. Processing circuitry 110 may then use the filteredsignal to determine a set of MAP values. Processing circuitry 110 canidentify a portion of the blood pressure signal indicating that thepatient is undergoing a cardiopulmonary bypass procedure and use amoving average to determine MAP values. In some examples, to determinethe set of MAP values, processing circuitry 110 determines signal peaksbased on convolving the filtered signal with a kernel.

In some examples, blood pressure signal indicator 320 may present MAPvalues as discrete points over time or in a table. Blood pressure signalindicator 320 may also present MAP values as a moving average orwaveform of discrete points. Blood pressure signal indicator 320 maypresent MAP values as a single value (e.g., a number) representing acurrent MAP value. Oxygen saturation signal indicator 310 and COx signalindicator 330 may also present rSO₂ values and COx values, respectively,as discrete points, in a table, as a moving average, as a waveform,and/or as a single value.

COx signal indicator 330 may present a set of correlation coefficientvalues determined by processing circuitry 110. Processing circuitry 110may determine the correlation coefficient values as a function of theoxygen saturation values presented in oxygen saturation signal indicator310 and the MAP values presented in blood pressure signal indicator 320.In some examples, a COx value at or near one indicates theautoregulation status of a patient is impaired, as shown inautoregulation status indicator 350.

Processing circuitry 110 may determine a set of correlation coefficientvalues and associated blood pressure values using the values presentedin indicators 310, 320, and/or 330. Processing circuitry 110 may beconfigured to determine an estimate of a limit of autoregulation basedon correlation coefficient values across a time window for datacollection. For example, the length of the time window may be one, two,or three hundred seconds.

COx value indicator 340 shows a COx value of 0.8, which may result in adetermination by processing circuitry 110 that the autoregulation statusof the patient is impaired. Processing circuitry 110 may be configuredto present, as the COx value in COx value indicator 340, the mostrecently determined COx value or a moving average of recently determinedCOx values. To determine the autoregulation status of a patient forpresentation in autoregulation status indicator 350, processingcircuitry 110 may determine whether the most recent MAP value shown inblood pressure signal indicator 320 is between the limits ofautoregulation presented in limit of autoregulation indicators 360 and370.

Processing circuitry 110 may present limit of autoregulation indicators360 and/or 370 in terms of blood pressure, for example, mmHg. Processingcircuitry 110 can determine the limits of cerebral autoregulation (LLAand ULA) for presentation in indicators 360 and 370 based on arelationship between the blood pressure of a patient and anotherphysiological parameter of the patient. For example, indicator 350and/or indicator 360 may be highlighted when the LLA has been exceededor indicator 360 may be highlighted when the ULA has been exceeded. Inother examples, a single indicator may present the type of limit thathas been exceed by the MAP value. If the LLA or ULA change, processingcircuitry 110 may control user interface 300 to change the value of theLLA or ULA in accordance with any change to that respective value.

Processing circuitry 110 may determine an estimate of a lower limit ofautoregulation presented in indicator 360 and/or an estimate of an upperlimit of autoregulation presented in indicator 370. Processing circuitry110 may determine the estimates based on a set of correlationcoefficient values including one or more updated values. Processingcircuitry 110 may be configured to generate a notification in responseto determining that the MAP value is less than or equal to the estimateof the lower limit of autoregulation. Processing circuitry 110 mayoutput the notification in autoregulation status indicator 350 as text,color, blinking, and/or any other suitable visible or audible manner.

FIGS. 4, 5, 6, 7, and 8 are example graphs showing bypass and pulsatile(e.g., non-bypass) signals. For each of FIGS. 4-8, the x-axis representstime, and the y-axis represents blood pressure, which may be expressedin millimeters of mercury. FIG. 4 shows a graph of arterial bloodpressure with periods 420, 430, and 440 of line flushing. During periods420, 430, and 440, the light gray line representing arterial bloodpressure can rise well beyond normal levels. For example, if the normallevels of blood pressure are less than one hundred and fifty millimetersof mercury, during periods 420, 430, and 440 the blood pressure may begreater than two hundred millimeters of mercury or greater than threehundred millimeters of mercury.

The graph of FIG. 4 also shows periods 400 and 410 where the arterialblood pressure has an intermittent signal. The graph shows no signalduring periods 400 and 410 because the processing circuitry of a device(e.g., device 100) or another blood pressure device can remove invalidregions and calculate MAP values only for the valid signal. Thus, duringperiods 400 and 410, the processing circuitry may determine that thesignal either has no blood pressure value or that the signal has a bloodpressure value that cannot be determined. In examples in which theprocessing circuitry identifies portions of the signal associated withperiods 400, 410, 420, 430, and 440, the processing circuitry canexclude the portions from the determination of a filtered signal. Theprocessing circuitry may be configured to modify some or all of theportions and use the modified blood pressure values to determine afiltered signal.

FIG. 5 shows a graph of a blood pressure signal with an abnormal heartbeat morphology changing into a regular heart beat morphology. Forexample, the blood pressure signal has an abnormal heart beat morphologyduring periods 500 and 540. The blood pressure signal has a normal heartbeat morphology during period 520. Time 510 is the dividing pointbetween periods 500 and 520, and time 530 is the dividing point betweenperiods 520 and 540. Processing circuitry that uses a beat-to-beatalgorithm for periods 500 and 540 may be less accurate than processingcircuitry that uses a moving average function. An example moving averagefunction may have a five-second window or a ten-second window.

Processing circuitry of this disclosure may be configured to usedifferent techniques for determining mean arterial pressure values forportions associated with different beat morphologies. For example,responsive to identifying period 500 as having an abnormal beatmorphology, the processing circuitry may be configured to determine meanarterial pressure values using a moving average. Responsive toidentifying period 500 as having a normal beat morphology, theprocessing circuitry may be configured to determine mean arterialpressure values using beat-to-beat information.

FIG. 6 shows a graph of a blood pressure signal with an irregularheartbeat morphology. During periods 600 and 610, the blood pressuresignal has sharp variations in the diastolic/systolic components. Theblood pressure signal also has sharp variations in the duration of eachheartbeat. For example, during period 620, the blood pressure signal hasa relatively high beat frequency. During period 630, the blood pressuresignal has a relatively low beat frequency. The blood pressure signalalso has a substantial amount of noise in the signal between period 600and period 610. Processing circuitry that uses a moving-averagealgorithm for the blood pressure signal shown in FIG. 6 to determine MAPvalues may more accurately determine the autoregulation status of thepatient, as compared to processing circuitry that uses a beat-to-beatalgorithm. For example, the abnormal beat morphologies and high noiselevels during periods 600 and 610 may result in less accurate meanarterial pressure values when using beat-to-beat techniques. Lessaccurate mean arterial pressure values can result in less accuratedeterminations of the autoregulation status of a patient.

FIG. 7 shows a graph of a blood pressure signal during bypass andnon-bypass periods. During period 700, the blood pressure signal has arelative high power level, which indicates that the patient is pulsatile(e.g., non-bypass). After time 710, the blood pressure signal has arelative low power level, which indicates that the patient is undergoinga cardiopulmonary bypass procedure. During period 720, the bloodpressure signal briefly has a relatively high power level, whichindicates that the patient is pulsatile. After time 730, the bloodpressure signal has a relative high power level, which indicates thatthe patient is again pulsatile and no longer undergoing bypass.Processing circuitry may be configured to apply an algorithm to theblood pressure signal shown in FIG. 7 to determine whether the patientis undergoing a cardiopulmonary bypass procedure. Processing circuitryof this disclosure can use a moving average to determine mean arterialpressure values for portions of the signal indicating a cardiopulmonarybypass procedure (e.g., between times 710 and 730), which may be moreaccurate than using beat-to-beat techniques.

FIG. 8 shows a graph of a blood pressure signal during bypass andnon-bypass periods. The graph of FIG. 8 shows a zoomed-in portion of theblood pressure signal shown in FIG. 7. During period 800, the bloodpressure signal has a relative high power level, which indicates thatthe patient is pulsatile. During period 820, the blood pressure signalhas a relatively low power level, which indicates that the patient isundergoing a cardiopulmonary bypass procedure. Time 810 is approximatelythe dividing line between periods 800 and 820. The power level of asignal may be proportional to the amplitude of the signal. Thus, theamplitude of the blood pressure signal is higher during period 800 thanthe amplitude during period 820.

Processing circuitry of this disclosure may be configured to determinewhether a patient is undergoing a cardiopulmonary bypass procedure basedon the power level of the blood pressure signal. The processingcircuitry can use other techniques for determining that the patient isundergoing a cardiopulmonary bypass procedure, such as the techniquesdescribed with respect to FIGS. 12-14. Responsive to the determinationof whether the patient is undergoing a cardiopulmonary bypass procedure,the processing circuitry can use different techniques for determiningmean arterial pressure values.

FIGS. 9A, 9B, 9C, and 9D are graphs illustrating example kernels. Eachof kernels 900, 910, 920, and 930 may be adaptable kernels. Theprocessing circuitry of a regional oximetry device or a blood pressuresensing device can select the parameters of kernels 900, 910, 920, and930 as a function of the heart rate of the patient. Each of kernels 900,910, 920, and 930 can be non-symmetrical with variable lengths that theprocessing circuitry can select according to signal morphology. Thex-axis of each graph of FIGS. 9A-9D represents the number of samples ofthe blood pressure signal, and the y-axis represents the relative weightof each sample.

The processing circuitry may be configured to use the blood pressuresignal to create kernels 900, 910, 920, and 930 to enhance or mitigatecertain beat features in the blood pressure signal. The processingcircuitry can use kernels 900, 910, 920, and 930 to determine trends ofthe mean arterial pressure of the patient. The determination of trendsin the mean arterial pressure may be useful, for example, for cardiacpatients with abnormal heart beat morphologies, as shown in FIGS. 5 and6.

In some examples, the processing circuitry creates and modifies kernels900, 910, 920, and 930 adaptively from the arterial blood pressuresignal. The processing circuitry can use the adaptive kernels 900, 910,920, and 930 for the detection and/or mitigation of unusual heart beatfeatures in the blood pressure signal. The processing circuitry mayselect or modify kernels 900, 910, 920, and 930 based on an estimate ofthe heart rate of the patient and/or based on other blood pressureparameters of the patient.

A kernel may include a number of phases, such as a first plateau, anupslope, a second plateau, a downslope, and a third plateau (e.g.,kernels 910, 920, and 930). The processing circuitry can dynamicallychange the number of phases of a kernel. The processing circuitry canselect the number of samples for each kernel phase based on beatmorphology, heart rate, harmonic decomposition, and/or any otherparameter of the blood pressure signal. The value of the kernel for eachphase may be predetermined or may be a function of the features andparameters of the blood pressure signal.

The processing circuitry can dynamically adjust the height or weighting(e.g., the y-axis direction of FIGS. 9A-9D) of each point on one ofkernels 900, 910, 920, and 930. In some examples, the processingcircuitry dynamically adjust the length (e.g., the x-axis direction ofFIGS. 9A-9D) of kernels 900, 910, 920, and 930, such that a kernel canbe adapted to the most likely beat duration. A single heart beat canlast thirty samples, forty samples, fifty samples, or any other numberof samples. In some examples, kernels 900, 910, 920, and 930 can also benon-symmetrical kernels that can emphasize and enhance universal beatcharacteristics. The processing circuitry can adapt the parameters usedto construct each non-symmetrical kernel.

The processing circuitry can use kernels to detect systolic peaks anddiastolic troughs in the blood pressure signal. A first kernel may havea higher cross-correlation with a first type of beat morphology, while asecond kernel may have a higher cross-correlation with a second type ofbeat morphology. Thus, the processing circuitry can convolve the bloodpressure signal with multiple kernels and use the kernel that best fitsor matches the beat morphology. The processing circuitry can select akernel and use the selected kernel to detect peaks and troughs. Theprocessing circuitry can also use the selected kernel to determine heartbeats and to determine the heart rate of the patient. The processingcircuitry may then determine mean arterial pressure values based on thepeaks, troughs, and heart beats.

The processing circuitry can also generate a quality metric from thedetermination of mean arterial pressure value. In examples in which morefiltering is required for a segment of the blood pressure signal, theprocessing circuitry may determine a lower quality metric, as comparedto a segment that requires less filtering. The processing circuitry canuse the quality metric to weight the confidence in the autoregulationalgorithm. In examples in which the quality metric is relatively high,then the processing circuitry can assign a higher confidence level to anestimate of the autoregulation status of the patient.

The processing circuitry can create several kernels to enhance detectionof systolic or diastolic peak components. Each kernel may enhance beatcharacteristics close to the point of interest, such as the systolicpeak or the diastolic trough. The processing circuitry can use onekernel in the extraction of the diastolic pressure and can use anotherkernel to extract the systolic pressure. The processing circuitry mayuse template matching where the template is predefined from a model ofthe range of possible heartbeat morphologies. The processing circuitrymay be configured to decompose the blood pressure signal into atime-frequency representation before using kernels 900, 910, 920, and930 or template matching.

FIGS. 10, 11, 12, 13, 14, 15, and 16 are flow diagrams illustratingexample techniques for determining a set of mean arterial pressurevalues, in accordance with some examples of this disclosure. AlthoughFIGS. 10-16 are described with respect to processing circuitry 110 ofdevice 100 (FIG. 1), in other examples, processing circuitry 210, 214,and/or 216 (FIG. 2), alone or in combination with processing circuitry110, may perform any part of the techniques of FIGS. 10-16.

In the example of FIG. 10, processing circuitry 110 receives a signalfrom sensing circuitry 141 indicative of a blood pressure of a patient(1000). The signal may include an arterial blood pressure signal of thepatient. Processing circuitry 110 then identifies at least one firstportion of the signal comprising a first characteristic of the signalexceeding a first threshold (1002). Processing circuitry 110 alsoidentifies at least one second portion of the signal comprising a secondcharacteristic of the signal exceeding a second threshold, the firstcharacteristic being different than the second characteristic (1004).

Processing circuitry 110 can process the signal to determine clean,valid segments for trend analysis. Processing circuitry 110 can usethese clean segments in the subsequent calculation of the mean arterialpressure. Processing circuitry 110 identifies the at least one firstportion and the at least one second portion as unclean segments forexclusion from the determination of mean arterial pressure. Processingcircuitry 110 can use a combination of signal features andcharacteristics to identify these portions, such as maximum allowableblood pressure values; minimum allowable blood pressure values;thresholds for the minimum and maximum differences between consecutivesamples (e.g., blood pressure variation values); portions of the signalwith a blood pressure derivative that is greater than a first thresholdrate or less than a second threshold rate, possibly with a minimum rangeor a maximum range (e.g., a total change) and possibly for a minimum ormaximum threshold time duration, or a combination of these values; arise above a minimum threshold followed by a drop by at least the sameamount in consecutive samples or over a finite time period; movingpercentile windows to identify high-diastolic (or low-systolic)components; a difference between the raw blood pressure signal and thefiltered signal above a certain threshold for a minimum or maximum timeduration. Table I shows example characteristics and thresholds.

TABLE I Example of characteristics and thresholds to identify signalportions to exclude or to modify. Characteristic Threshold Example ValueBlood pressure value Maximum blood pressure value 250 mmHg Bloodpressure value Minimum blood pressure value 10 mmHg Difference in bloodpressure Blood pressure variation value (e.g., 50 mmHg between twoconsecutive portions maximum jump) Monotonic change Blood pressurevariation value (e.g., 160 mmHg maximum change) Difference in bloodpressure Maximum spike 20 mmHg between three consecutive portionsFiltering frequency Low-pass cutoff frequency 20 Hertz Differencebetween raw signal and Maximum difference to filtered signal 3 mmHglow-pass-filtered signal Range of deviation Predetermined range ofdeviation 1 mmHg Range of deviation Minimum number of samples for 5samples constant signal Diastolic value of signal Blood pressurethreshold value 130 mmHg Diastolic value of signal Percentile value10^(th) percentile

Table II shows other parameters for processing the raw blood pressuresignal.

TABLE II Example parameters for identifying portions of the signalParameter Example Value Low-pass cutoff frequency 20 Hertz Maximumdifference between 3 mmHg raw signal and low-pass-filtered signalMaximum block size for recovery of signal 10 samples (interpolation ofmissing or excluded portions) Identify constant 5 samples signal -minimum # samples Maximum diastolic 130 mmHg

Processing circuitry 110 determines a filtered signal indicative of theblood pressure of the patient by excluding the at least one firstportion and the at least one second portion from the signal (1006). Insome examples, processing circuitry 110 modifies an identified portioninstead of excluding the identified portion. In examples in which anexcluded portion is less than a maximum block size, processing circuitry110 can recovery the signal by interpolating (e.g., linearlyinterpolating) the surrounding portions of the signal. Processingcircuitry 110 can determine the filtered signal based on the portions ofthe signal that were not excluded, along with the modified portions ofthe signal.

Processing circuitry 110 determines a set of mean arterial pressurevalues based on the filtered signal (1008). Processing circuitry 110 candetermine a mean arterial pressure value based on an average (e.g., aweighted average) of the systolic and diastolic values of the signal.Processing circuitry 110 then determines an autoregulation status of thepatient based on the set of mean arterial pressure values (1010).Processing circuitry 110 can use the mean arterial pressure values todetermine correlation coefficient values. Additional example details ofusing mean arterial pressure values to determine an autoregulationstatus may be found in commonly assigned U.S. patent application Ser.No. 15/980,235, filed on May 15, 2018, entitled “Determining a Limit ofAutoregulation,” the entire contents of which is incorporated herein byreference in its entirety.

Processing circuitry 110 can use a cleaning algorithm that includesidentifying artifacts in the raw signal to clean the signal, identifyingbypass and pulsatile portions of the signal, and calculating the meanarterial pressure values. For portions identified as pulsatile,processing circuitry 110 can calculate mean arterial pressure valuesusing beat-to-beat information from the signal. For portions thatindicate that the patient is undergoing a cardiopulmonary bypassprocedure, processing circuitry 110 can calculate mean arterial pressurevalues using a moving average of the signal.

To determine a filtered signal, processing circuitry 110 can identifyartifacts in the raw signals by identifying portions of the signalincluding a characteristic of the signal that exceeds a threshold.Events that can cause artifacts include electrocautery, transducershifts, line flushing, AC interference, and probe movement. Processingcircuitry 110 can identify portions of the blood pressure signal thatinclude artifacts. Processing circuitry 110 can pass or set acorresponding flag for the identified portions. Processing circuitry 110may activate an alarm or process a more accurate estimate of the meanarterial pressure based on the flags. For example, processing circuitry110 can exclude or modified the flagged portions of the signal.

Processing circuitry 110 can recover a portion responsive to making apositive identification of a number of features. Processing circuitry110 can also recursively identify new issues with the fixed signal.Processing circuitry 110 may be configured to recover a portion of thesignal only under some conditions, such as when number of consecutiveidentified samples is above or below a threshold.

Processing circuitry 110 may be configured to detect artifacts thataffect autoregulation by using a combination of parameters andcharacteristics along with signal processing techniques to process theraw blood pressure signal or the cleaned BP signal. These signalprocessing techniques can include filtering, interpolation, anddetection of portions of the signal that include undesiredcharacteristics (e.g., characteristics that exceed thresholds).Processing circuitry 110 may apply machine learning methods to evaluatethe set of optimal parameters and thresholds to identify portions forcleaning. Processing circuitry 110 can calculate the characteristics andthresholds parametrically from real patient data, such as historicalpatient data.

Processing circuitry 110 can identify a portion of the blood pressuresignal including a first derivative of the signal that is greater than athreshold for a predetermined time duration. In examples in which thefirst derivative of the signal exceeds the threshold may indicate a lineflushing event. To detect a catheter adjustment, processing circuitry110 can use a moving window to detect a shift of the signal immediatelybefore and immediately after the adjustment. Processing circuitry 110may be configured to apply, for example, a Heaviside function or someother function that can identify shifts in the blood pressure values ofthe signal.

In the example of FIG. 11, processing circuitry 110 receives a signalindicative of a blood pressure of a patient (1100). Processing circuitry110 then identifies a first set of erroneous portions of the signal forexclusion (1102). Processing circuitry 110 also identifies a second setof erroneous portions of the signal for modification (1104). Althoughprocesses 1102, 1104, and 1106 are shown as occurring simultaneously,these processes may be performed iteratively in other examples.Processing circuitry 110 may identify portions of the raw arterial bloodpressure signal with artifacts and clean the raw signal to conduct abeat-to-beat analysis. Processing circuitry 110 can use the beat-to-beatanalysis to generate a filtered signal. Processing circuitry 110 may usea window of the blood pressure signal with a duration of ten seconds anda sampling rate of 100 hertz. Processing circuitry 110 can identifyportions of the signal including a characteristic of the signal thatexceeds a threshold for excluding or modifying.

For example, processing circuitry 110 can identify portions forexclusion or modification by determining that a blood pressure value isless than a minimum blood pressure value, where the minimum bloodpressure value may be less than or equal to 25 mmHg. An example minimumblood pressure value is 20 mmHg. Processing circuitry 110 can identify aportion for exclusion or modification by determining that a bloodpressure value is greater than a maximum blood pressure value, where themaximum blood pressure value may be greater than or equal to 200 mmHg.An example maximum blood pressure value is 250 mmHg.

Processing circuitry 110 can identify a portion for exclusion ormodification by determining that a difference in blood pressure betweentwo consecutive portions of the signal is greater than a blood pressurevariation value. The blood pressure variation value may be greater thanor equal to 40 mmHg, where an example blood pressure variation value is50 mmHg. Processing circuitry 110 can identify a portion for exclusionor modification by determining that a derivative of the at least onefirst portion of the signal exceeds a threshold rate for longer than athreshold time duration for a total change in the signal greater than ablood pressure variation value.

Processing circuitry 110 can identify a portion for exclusion ormodification by determining that an increase (or decrease) inconsecutive samples of at least 20 mmHg followed by a decrease (orincrease) in consecutive samples of at least 20 mmHg. Processingcircuitry 110 can identify a portion for exclusion or modification bydetermining that at least five consecutive samples have the same valueor are within a predetermined range of deviation. Processing circuitry110 can identify a portion for exclusion or modification by determininga continuous (e.g., monotonic) increase or decrease in blood pressurevalues of at least 160 mmHg.

Processing circuitry 110 can identify a portion for exclusion ormodification by determining a difference of at least 3 mmHg between theraw signal and a low-pass-filtered signal. Processing circuitry 110 canidentify a portion for exclusion or modification by determining that theportion has a high level of noise, such a standard deviation of thederivative of the raw signal that exceeds 15 mmHg per second. Processingcircuitry 110 can identify a portion for exclusion or modification bydetermining that the tenth percentile value of the raw signal over aone-second duration exceeds 130 mmHg. Processing circuitry 110 canidentify a portion for exclusion or modification by determining that theblood pressure value continuously increases or continuously decreasesfor more than one second.

Processing circuitry 110 may be configured to independently tag eachidentified portion. After processing circuitry 110 has processed all ofthe rules to identify the affected samples, processing circuitry 110 cantag small regions of signal (e.g., less than one-tenth of one second) asa spike or as high-frequency noise (e.g., the second set of erroneousportions). Processing circuitry 110 may be configured to remove thesecond set of erroneous portions. Processing circuitry 110 can thenreplace the second set of erroneous portions with cleaned portions(1106). The cleaned portions may be linearly interpolated from the bloodpressure values surrounding the tagged portion. Processing circuitry 110may replace an identified portion of the blood pressure signal with aninterpolated portion, where the identified portion is less thanthreshold time duration, such as 0.1, 0.2, 0.5, or 1.0 seconds. Thus,processing circuitry 110 can recover the second set of erroneousportions that might have been corrupted.

Processing circuitry 110 determines the union of all erroneous portionsof the signal (1108). Processing circuitry 110 can then exclude theerroneous portions of the signal that still include a characteristicthat exceed a threshold (1110). Processing circuitry 110 may identifyand exclude isolated, untagged portions of the signal that span lessthan one second. Processing circuitry 110 can generate the filteredsignal based on the remaining portions and the cleaned portions of thesignal (1112).

In the example of FIG. 12, processing circuitry 110 receives a signalindicative of a blood pressure of a patient (1200). Processing circuitry110 then removes artifacts to clean the signal (1202). Processingcircuitry 110 can remove artifacts by identifying portions that includea characteristic that exceeds a threshold. Processing circuitry 110 canalso determine whether a portion of the signal indicates acardiopulmonary bypass procedure (1204). Given a window of ten secondsof the signal, processing circuitry 110 classifies the window asindicating a bypass procedure, according to the prediction parameters,which may be calculated from training data.

Responsive to determining that the portion of the signal does notindicate the cardiopulmonary bypass procedure, processing circuitry 110determines mean arterial pressure values for the portion by integratingthe blood pressure values beat by beat (1206). The beat by beatintegration may include a weighted average of the systolic and diastolicblood pressure values of the signal, or any other beat by beat method ofdetermining a mean arterial pressure value. Responsive to determiningthat the portion of the signal indicates the cardiopulmonary bypassprocedure, processing circuitry 110 determines mean arterial pressurevalues for the portion based on a moving average of the blood pressurevalues of the raw signal (1208). The moving average may include anaverage of the samples across a five- or ten-second window. Processingcircuitry 110 then outputs the set of mean arterial pressure values, forexample, for presentation via a display (1210).

FIG. 13 shows an example process for identifying a portion of a bloodpressure signal as indicating a cardiopulmonary bypass procedure. Thus,processing circuitry 110 can use the example process shown in FIG. 13 toperform step 1204 shown in FIG. 12. In the example of FIG. 13,processing circuitry 110 receives a signal indicative of a bloodpressure of a patient (1300). Processing circuitry 110 can calculate apower spectrum of the signal, which may be computationally expensive.Thus, as initial conditions, processing circuitry 110 can check someinitial conditions to avoid the processing-intensive task of calculatingthe power spectrum. Processing circuitry 110 may determine whether thepower of the signal is greater than a threshold (1302). For example,processing circuitry 110 can determine whether the square root of themean power of the signal is larger than 50.0 arbitrary units (a.u.).Responsive to determining that the power of the signal is greater thanthe threshold, processing circuitry 110 determines that the portion ofthe signal does not indicate a cardiopulmonary bypass procedure (1314).

Processing circuitry 110 can also determine whether the diastolicpressure of the signal is greater than a threshold (1304). For example,processing circuitry 110 may determine whether the absolute diastolicvalue, which may be defined as the fifth percentile of the unfilteredraw signal window, is greater than 110 mmHg. Responsive to determiningthat the diastolic pressure of the signal is greater than the threshold,processing circuitry 110 determines that the portion of the signal doesnot indicate a cardiopulmonary bypass procedure.

If neither of these steps indicates a cardiopulmonary bypass procedure,processing circuitry 110 performs a spectral analysis of the signal(1306) and determines whether the power around the heart rate frequencyis less than a threshold (1308). For example, processing circuitry 110can determine whether the total power in a frequency band around theestimated heart rate frequency is larger than 9.0 a.u. Processingcircuitry 110 can also determine whether the estimated heart rate isless than a predetermined heart rate, such as 90 beats per minutes.Responsive to determining that the power around the heart rate frequencyis less than the threshold and the estimated heart rate is less than thepredetermined heart rate.

If none of the above conditions are met, processing circuitry 110 mayuse trained parameters to generate a prediction value (1310). Forexample, processing circuitry 110 can calculate a set of features forthe prediction value. Processing circuitry 110 can classify a window asindicating a bypass procedure responsive to determining that theprediction value is greater than 0.5. Processing circuitry 110 canclassify a window as not indicating the bypass procedure responsive todetermining that the prediction value is less than 0.5.

Processing circuitry 110 can calculate the parameters for determiningthe prediction value based on a machine learning approach (e.g.,logistic regression) using several features of the blood pressuresignals. Examples of the features that processing circuitry 110 can useare power, diastolic pressure, heart rate, and frequencies derived froma power analysis. The prediction value is a single number calculated byprocessing circuitry 110 by combining these features in a predeterminedequation that uses the trained parameters. The trained parameters arethe result of processing circuitry 110 training the algorithms describedherein. Processing circuitry 110 can classify a portion of the signal asindicating a cardiopulmonary bypass procedure according to theprediction value.

For example, processing circuitry 110 can classify a portion asindicating a cardiopulmonary bypass procedure if the prediction value isgreater than 0.5 and classify the portion as non-bypass if theprediction value is not greater than 0.5. Processing circuitry 110 canuse the machine learning training to determine a parameter for thefeature “HR” (e.g., heart rate) equal to 0.15 and a parameter for thefeature “power” equal to 0.5. Processing circuitry 110 can use Equation(1) to calculate the prediction value. Equation (1) has only twofeatures, but processing circuitry 110 can use more than two features insome examples.

Prediction value=0.15×HR_(current)+0.5×power_(current)  (1)

FIG. 14 shows an example process for identifying a portion of a bloodpressure signal as indicating a cardiopulmonary bypass procedure. Thus,processing circuitry 110 can use the example process shown in FIG. 14 toperform step 1204 shown in FIG. 12. FIG. 14 shows another exampleprocess for determining whether a portion of a blood pressure signalindicates a cardiopulmonary bypass procedure. In the example of FIG. 14,processing circuitry 110 receives a signal indicative of a bloodpressure of a patient (1400). Processing circuitry 110 then determineswhether the median pressure of a portion of the signal is greater than120 mmHg (1402). Responsive to determining that the median pressure of aportion of the signal is greater than 120 mmHg, processing circuitry 110determines that the portion of the signal does not indicate acardiopulmonary bypass procedure (1416).

Responsive to determining that the median pressure of a portion of thesignal is not greater than 120 mmHg, processing circuitry determines ahigh percentile value minus a low percentile value (1404). For example,processing circuitry 110 can subtract the fifth percentile value fromthe 95th percentile value. Responsive to determining that the differenceis greater than ten mmHg, processing circuitry 110 determines that theportion of the signal does not indicate a cardiopulmonary bypassprocedure (1416). Responsive to determining that the difference is notgreater than ten mmHg, processing circuitry 110 performs a spectralanalysis of the signal (1408).

Processing circuitry 110 then determines whether the power around theheart rate frequency is greater than a threshold (1410). For example,processing circuitry 110 can determine whether the power in a frequencyband between 0.5 and 2.0 hertz is greater than 2 a.u. Responsive todetermining that the power around the heart rate frequency is greaterthan the threshold, processing circuitry 110 determines that the portionof the signal does not indicate a cardiopulmonary bypass procedure.Responsive to determining that the power around the heart rate frequencyis not greater than the threshold, processing circuitry 110 determineswhether the power around the pump frequency is greater than a threshold(1412). For example, processing circuitry 110 can determine whether thepower in a frequency band between two and five hertz is greater than onea.u. Responsive to determining that the power around the pump frequencyis not greater than the threshold, processing circuitry 110 determinesthat the portion of the signal does not indicate a cardiopulmonarybypass procedure.

Responsive to determining that the power around the pump frequency isgreater than the threshold, processing circuitry 110 determines whetherthe power around the pump frequency and the non-direct-current power areconsistent (1414). Responsive to determining that the power around thepump frequency and the non-direct-current power are consistent,processing circuitry 110 determines that the portion of the signal doesnot indicate a cardiopulmonary bypass procedure. Responsive todetermining that the power around the pump frequency and thenon-direct-current power are not consistent, processing circuitry 110determines that the portion of the signal indicates a cardiopulmonarybypass procedure (1418).

Processing circuitry 110 can detect portions indicating acardiopulmonary bypass procedure by using a specific kernel orcombination of kernels to enhance bypass characteristics. Processingcircuitry 110 can classify the portion as bypass or non-bypass by usingthe kernel(s) to detect frequencies around the bypass pump frequency.Processing circuitry 110 can identify samples above a threshold toexclude portions of the signal that may not belong to bypass. Processingcircuitry 110 may calculate the amplitude of oscillations, or aninterpercentile difference, to classify the portion of signal as bypassor not bypass. Processing circuitry 110 can perform a power analysis ofthe signal via a Fourier Transform or a Least-Square Spectral Analysisaround specified frequencies such as the expected heart-rate frequencyand/or expected bypass pump frequency. Processing circuitry 110 can usethe power analysis to detect lack of heart-beat and existence of abypass pump. Processing circuitry 110 may use a combination ofthresholds to evaluate the power around each frequency to classify theportion as indicating bypass or non-bypass.

In some examples, processing circuitry 110 can post-process the bypassclassifier output by setting the bypass flag for some time period beforeprocessing circuitry 110 declares that a portion of the signal indicatesbypass in order to reduce the number of false positives. Sometimes,bypass periods can show small amplitude pulsation caused by the bypasspump mechanism. These pump-induced oscillations may vary, with differentfrequencies and different amplitudes. Processing circuitry 110 maycalculate and/or display the autoregulation status only during bypass oronly during non-bypass.

Processing circuitry 110 can use the bypass algorithm describe hereinalong with any method for determining the autoregulation status of apatient. In addition, processing circuitry 110 can use the bypassalgorithm describe herein along with any method for determining a set ofmean arterial pressure values.

Responsive to determining that a portion of the signal indicates acardiopulmonary bypass procedure, processing circuitry 110, processingcircuitry 110 can apply a moving average with a duration of one secondto the elements in the ten-second window in steps of 0.5 seconds.Processing circuitry 110 can calculate the diastolic and systolic valuesduring a bypass procedure as the 5th and 95th percentile values of thesame one-second moving window. In examples in which the one-secondmoving window contains invalid samples, processing circuitry 110performs the calculations on the available valid samples. In examples inwhich all the samples in one-second window are invalid, the calculationfor that period will trigger a flag indicating an invalid mean arterialpressure value. Processing circuitry 110 may identify the heart-rateduring this portion of the signal as not available. Processing circuitry110 can associate a bypass flag with each of half-second steps.

FIG. 15 shows an example process for determining mean arterial pressurevalues by convolving a blood pressure signal with one or more kernels.Processing circuitry 110 can use a kernel to identify systolic peaks,diastolic troughs, and heart beats from the blood pressure signal.Processing circuitry 110 can use the peaks, troughs, and heart beats inthe determination of mean arterial pressure values. In the example ofFIG. 15, processing circuitry 110 convolves blood pressure values of afiltered signal with two or more kernels and selects the convolvedsignal with the highest power (1500). For example, processing circuitry110 can perform a convolution of the cleaned signal with six differentkernels, where each kernel enhances beat characteristics at differentfrequencies. The frequencies may be 0.75, 1.0, 1.25, 1.5, 2.0, and 2.5hertz. These frequencies may represent the range of frequencies seen intraining data, and are able to capture the expected range of frequenciesexpected in human population, which is 30 to 240 beats per minutes.Processing circuitry 110 can use kernels with zero-mean and a totalpower that is normalized to one. After processing circuitry 110 applieseach kernel to the filtered signal, processing circuitry 110 calculatesthe square root of the average power of the kernel-filtered signals(e.g., the convolved signals). Processing circuitry 110 may then selectthe convolved signal with the highest power for further analysis.

In the example of FIG. 15, processing circuitry 110 detects peaks in theselected signal (1502). Processing circuitry 110 can also detect troughsin the selected signal as the minimums between the peaks (1504).Processing circuitry 110 can apply a peak detection algorithm to theselected filtered signal. Processing circuitry 110 may initially apply avery strict criteria to detect peaks. Processing circuitry 110 may thendetermine the quality of the peak and trough detection (1506).

Responsive to determining that the signal does not have a high qualitylevel, processing circuitry 110 can convolve the filtered signal withadditional kernels or select a different convolved signal of the sixconvolved signals. Processing circuitry 110 may also reattempt peakdetection if the signal did not pass the strict criteria for peakdetection. Processing circuitry 110 can check if there is a moresuitable filtering to use in the window of the blood pressure signal.Processing circuitry 110 may re-estimate the heartrate using spectralanalysis and use less conservative parameters for peak detection. Inthis way, processing circuitry 110 can detect less conventionalwaveforms, for example the waveforms that occur in arrythmia, or thewaveforms that occur when a precocious ventricular ejection occurs.

In some examples, the signal may pass this strict criteria andprocessing circuitry 110 can determine the signal has very good quality,which may mean no missing peaks and consistent interpeak distance andpeak heights. Responsive to determining that the signal has a highquality level, processing circuitry 110 may determine that the detectedpeaks are a good estimate for the systolic peaks in the blood pressuresignal. Processing circuitry 110 may then detect heart beats, systolicvalues, and diastolic values in the selected signal (1508). Processingcircuitry 110 can perform a lookup for the blood pressure systolic peakson the original (e.g., unfiltered) signal. Processing circuitry 110 mayidentify the diastolic values as the minimum of the sample pointsbetween each peak.

Once the peaks and troughs have been identified in the ABP signal,processing circuitry 110 can perform a final check on the beat qualityby looking at the median of beat duration and the median distance ortime to the median. Processing circuitry 110 can flag a beat as invalidif the beat falls outside the criteria established for the beat quality,such as a duration between 0.1 and 2.0 seconds, having a duration thatis less than 150% of the median beat duration, and valid sample values.Processing circuitry 110 may integrate the convolved signal usingbeat-to-beat information (1510). Processing circuitry 110 can calculatemean arterial pressure values by integrating the samples that constitutethe beat. This integration can generate a more accurate value for meanarterial pressure than simply finding the mean of the samples thatconstitute the beat. Performing a simple average can sometimes introducean error in mean arterial pressure that is a function of the heart rate.This can be significant for sufficiently high heart rates.

Processing circuitry 110 can define a time stamp for each beat as themiddle point between both diastolic troughs. Processing circuitry 110may define the beginning and ending of each beat as the diastolictroughs. Processing circuitry 110 may be configured to calculate thediastolic value associated with each beat as the mean of the start andend points that define the beat. Processing circuitry 110 can alsocalculate the systolic value associated with the beat during the peakdetection step. Processing circuitry 110 can calculate the heart rateassociated with the beat as the inverse of the beat duration. Inexamples in which an error occurs during the processing of the beat,processing circuitry 110 may return an associated flag. Processingcircuitry 110 may then output the set of mean arterial pressure valuesbased on the integration of the convolved signal (1512).

FIG. 16 shows an example process for determining mean arterial pressurevalues by convolving a blood pressure signal with specialized kernels.Processing circuitry 110 can use each kernel to identify one or moreparameters such as systolic peaks, diastolic troughs, and heart beatsfrom the blood pressure signal. Processing circuitry 110 can use thepeaks, troughs, and heart beats in the determination of mean arterialpressure values. FIG. 16 shows an example process for using dynamickernels to enhance or mitigate unusual beat morphologies. In the exampleof FIG. 16, processing circuitry 110 estimates beat duration andharmonic decomposition in a window of the cleaned blood pressure signal(1600). The harmonic decomposition may include a power spectral analysisand/or a frequency analysis. Processing circuitry 110 then determines anumber of kernel phases based on window information such as beatmorphology, harmonics, and other information (1602). One or more of thekernels may include a plateau, upslope, plateau, downslope and plateau(see, e.g., the kernels shown in FIGS. 9A-9D). Processing circuitry 110determines multiple coefficients based on the beat duration andmorphology (1604).

Processing circuitry 110 may create a systolic peaks kernel fordetecting systolic peaks and a diastolic troughs kernel for detectingdiastolic troughs (1606). Processing circuitry 110 can also create aGaussian kernel (1608) and a symmetrical steps kernel (1610). FIG. 9Ashows an example of a Gaussian kernel, FIG. 9D shows an example of asymmetrical steps kernel. Processing circuitry 110 may be configured toconvolve the filtered signal with the kernels to generate convolvedsignals. Processing circuitry 110 may then output the convolved signalswith the calculated kernels (1612). The output may also include meanarterial pressure values determined by processing circuitry 110 usingthe kernels.

As processing circuitry 110 receives a stream of the blood pressuresignal from sensing circuitry 141, processing circuitry 110 can processthe results in a pulsatile (e.g., non-bypass) mode or in a bypass mode,which may use a one-second moving average. Processing circuitry 110 canrecord and combine mean arterial pressure values for the last tenseconds of data. There is an overlap in the moving ten-second window,such that processing circuitry 110 may update only the mean arterialpressure values with timestamps that occur after the last validcalculated value. Using this technique, processing circuitry 110 canavoid updating older values in the buffer with more recent mean arterialpressure values due to the overlap. Thus, previous calculated values maynot change during the updating process.

Processing circuitry 110 can output the raw beat-to-beat calculationsand bypass calculations. Each beat-to-beat calculation may be associatedwith a timestamp and with corresponding flags. Processing circuitry 110can average the available mean arterial pressure values from the lastten seconds, both from pulsatile calculations and bypass calculations.Processing circuitry 110 can average each mean arterial pressure valuewith a weight proportional to the duration of the mean arterial pressurevalue. By weighting the mean arterial pressure values, processingcircuitry 110 may avoid overrepresenting short beats. Processingcircuitry 110 can perform this calculation every second (e.g., onehertz). Processing circuitry 110 can output this calculation as thefinal mean arterial pressure output. The final output may include theten-second average from mean arterial pressure. Correspondingly, thediastolic and systolic values are also output, as well as a flag valuesassociated with each window.

The following numbered examples demonstrate one or more aspects of thedisclosure.

Clause 1: In some examples, a device includes processing circuitryconfigured to receive a signal indicative of a blood pressure of thepatient and identify at least one first portion of the signal includinga first characteristic of the signal exceeding a first threshold. Theprocessing circuitry is also configured to identify at least one secondportion of the signal including a second characteristic of the signalexceeding a second threshold, the first characteristic being differentthan the second characteristic. The processing circuitry is furtherconfigured to determine a filtered signal indicative of the bloodpressure of the patient by excluding the at least one first portion andthe at least one second portion from the signal. The processingcircuitry is configured to determine a set of mean arterial pressurevalues based on the filtered signal and determine an autoregulationstatus of the patient based on the set of mean arterial pressure values.

Clause 2: In some examples of clause 1, the device further includessensing circuitry configured to generate a signal indicative of a bloodpressure of a patient.

Clause 3: In some examples of clause 1 or clause 2, the firstcharacteristic includes a blood pressure value.

Clause 4: In some examples of any of clauses 1-3, the processingcircuitry is configured to identify the at least one first portion ofthe signal at least in part by determining that the first characteristicis one of less than a minimum blood pressure value or greater than amaximum blood pressure value.

Clause 5: In some examples of any of clauses 1-4, the processingcircuitry is configured to determine that the first characteristic isless than the minimum blood pressure value at least in part bydetermining that the blood pressure value of the patient is less thantwenty millimeters of mercury.

Clause 6: In some examples of any of clauses 1-5, the minimum bloodpressure value is less than or equal to twenty-five millimeters ofmercury.

Clause 7: In some examples of any of clauses 1-6, the processingcircuitry is configured to determine that the first characteristic isgreater than the maximum blood pressure value at least in part bydetermining that the blood pressure value is greater than two hundredand fifty millimeters of mercury.

Clause 8: In some examples of any of clauses 1-7, the maximum bloodpressure value is greater than or equal to two hundred millimeters ofmercury.

Clause 9: In some examples of any of clauses 1-8, the processingcircuitry is configured to identify the at least one first portion ofthe signal at least in part by determining that a difference in bloodpressure between two consecutive portions of the signal is greater thana blood pressure variation value.

Clause 10: In some examples of any of clauses 1-9, the processingcircuitry is configured to determine that the difference in bloodpressure between the two consecutive portions is greater than the bloodpressure variation value at least in part by determining that thedifference in blood pressure between the two consecutive portions isgreater than fifty millimeters of mercury.

Clause 11: In some examples of any of clauses 1-10, the blood pressurevariation value is greater than or equal to forty millimeters ofmercury.

Clause 12: In some examples of any of clauses 1-11, the processingcircuitry is configured to identify the at least one first portion ofthe signal at least in part by determining that a derivative of thesignal exceeds a threshold rate for longer than a threshold timeduration for a total change in the signal greater than a blood pressurevariation value.

Clause 13: In some examples of any of clauses 1-12, the processingcircuitry is configured to determine that the derivative of the at leastone first portion of the signal exceeds the threshold rate for longerthan the threshold time duration for a total change greater than theblood pressure variation value at least in part by determining that thederivative of the at least one first portion of the signal is greaterthan negative ten millimeters of mercury per second for longer than thethreshold time duration for the total change greater than the bloodpressure variation value.

Clause 14: In some examples of any of clauses 1-13, the threshold rateis greater than or equal to negative fifteen millimeters of mercury persecond.

Clause 15: In some examples of any of clauses 1-14, the processingcircuitry is configured to determine that the derivative of the at leastone first portion of the signal exceeds the threshold rate for longerthan the threshold time duration for a total change greater than theblood pressure variation value at least in part by determining that thederivative of the at least one first portion of the signal is less thanpositive ten millimeters of mercury per second for longer than thethreshold time duration for the total change greater than the bloodpressure variation value.

Clause 16: In some examples of any of clauses 1-15, the threshold rateis less than or equal to positive fifteen millimeters of mercury persecond.

Clause 17: In some examples of any of clauses 1-16, the processingcircuitry is configured to determine that the derivative of the at leastone first portion of the signal exceeds the threshold rate for longerthan the threshold time duration for a total change greater than theblood pressure variation value at least in part by determining that thederivative of the at least one first portion of the signal exceeds thethreshold rate for longer than one second for the total change greaterthan the blood pressure variation value.

Clause 18: In some examples of any of clauses 1-17, the threshold timeduration is greater than or equal to five hundred milliseconds.

Clause 19: In some examples of any of clauses 1-18, the processingcircuitry is configured to determine that the derivative of the at leastone first portion of the signal exceeds the threshold rate for longerthan the threshold time duration for a total change greater than theblood pressure variation value at least in part by determining that thederivative of the at least one first portion of the signal exceeds thethreshold rate for longer than the threshold time duration for the totalchange greater than twenty millimeters of mercury.

Clause 20: In some examples of any of clauses 1-19, the blood pressurevariation value is greater than or equal to fifteen millimeters ofmercury.

Clause 21: In some examples of any of clauses 1-20, the processingcircuitry is configured to identify the at least one first portion ofthe signal at least in part by determining that a number of consecutivesamples of the blood pressure of the patient are within a predeterminedrange of deviation.

Clause 22A: In some examples of any of clauses 1-21, the processingcircuitry is configured to determine that the number of consecutivesamples are within the predetermined range of variation at least in partby determining that five consecutive samples are within a range ofvariation of one millimeter of mercury.

Clause 22B: In some examples of any of clauses 1-22A, the predeterminedrange of variation is less than or equal to two millimeters of mercury.

Clause 23: In some examples of any of clauses 1-22B, the processingcircuitry is configured to identify the at least one first portion ofthe signal at least in part by determining that a monotonic change inthe blood pressure of the patient of the signal is greater than a bloodpressure variation value.

Clause 24: In some examples of any of clauses 1-23, the processingcircuitry is configured to determine that the monotonic change isgreater than a blood pressure variation value at least in part bydetermining that the monotonic change is greater than one hundred andsixty millimeters of mercury.

Clause 25: In some examples of any of clauses 1-24, the blood pressurevariation value is greater than or equal to one hundred and fortymillimeters of mercury.

Clause 26: In some examples of any of clauses 1-25, the processingcircuitry is configured to identify the at least one first portion atleast in part by determining that the monotonic change occurs for morethan one second.

Clause 27: In some examples of any of clauses 1-26, the processingcircuitry is configured to identify the at least one first portion atleast in part by determining that the monotonic change occurs for morethan a threshold time duration, where the threshold time duration isgreater than or equal to eight hundred milliseconds.

Clause 28: In some examples of any of clauses 1-27, the processingcircuitry is configured to identify the at least one first portion ofthe signal at least in part by determining that a level of noise of thesignal is greater than the first threshold level.

Clause 29: In some examples of any of clauses 1-28, the processingcircuitry is configured to determine that the level of noise of thesignal is greater than the first threshold level at least in part bydetermining that a standard deviation of a derivative of the signal isgreater than fifteen millimeters of mercury per second.

Clause 30: In some examples of any of clauses 1-29, the processingcircuitry is configured to determine that the level of noise of thesignal is greater than the first threshold level at least in part bydetermining that a standard deviation of a derivative of the signal isgreater than a threshold rate, where the threshold rate is greater thanor equal to twelve millimeters of mercury per second.

Clause 31: In some examples of any of clauses 1-30, the processingcircuitry is configured to identify the at least one first portion ofthe signal at least in part by determining that a diastolic value of thesignal is greater than a blood pressure threshold value.

Clause 32: In some examples of any of clauses 1-31, the processingcircuitry is configured to determine that the diastolic value is greaterthan the blood pressure threshold value at least in part by determiningthat the diastolic value of the signal is greater than one hundred andthirty millimeters of mercury.

Clause 33: In some examples of any of clauses 1-32, the blood pressurethreshold value is greater than or equal to one hundred and twentymillimeters of mercury.

Clause 34: In some examples of any of clauses 1-33, the processingcircuitry is configured to determine that the diastolic value within thefirst portion is greater than the blood pressure threshold value atleast in part by determining that a percentile value is greater than theblood pressure threshold value.

Clause 35: In some examples of any of clauses 1-34, the percentile valueis a tenth percentile of the signal.

Clause 36: In some examples of any of clauses 1-35, the percentile valueis less than or equal to a fifteenth percentile of the signal.

Clause 37: In some examples of any of clauses 1-36, the processingcircuitry is configured to identify the at least one first portion atleast in part by determining that a diastolic value of the signal isgreater than a blood pressure threshold value for a threshold timeduration.

Clause 38: In some examples of any of clauses 1-37, the threshold timeduration is less than or equal to two seconds.

Clause 39: In some examples of any of clauses 1-38, the processingcircuitry is further configured to identify at least one third portionof the signal including a third characteristic of the signal exceeding athird threshold.

Clause 40: In some examples of any of clauses 1-39, the processingcircuitry is configured to determine the filtered signal at least inpart by modifying the at least one third portion of the signal.

Clause 41: In some examples of any of clauses 1-40, the processingcircuitry is configured to identify the at least one third portion atleast in part by determining that a difference in blood pressure betweena first consecutive portion of the signal and a second consecutiveportion of the signal is greater than a first blood pressure variationvalue and determining that a difference in blood pressure between thesecond consecutive portion of the signal and a third consecutive portionof the signal is greater than a second blood pressure variation value.

Clause 42: In some examples of any of clauses 1-41, the processingcircuitry is configured to determine that the first sample is greaterthan the second sample by at least the first blood pressure thresholdvalue at least in part by determining that the first sample is greaterthan the second sample by at least twenty millimeters of mercury, andthe processing circuitry is configured to determine that the secondsample is greater than the third sample by at least the second bloodpressure threshold value at least in part by determining that the secondsample is greater than the third sample by at least twenty millimetersof mercury.

Clause 43: In some examples of any of clauses 1-42, the processingcircuitry is configured to modify the at least one third portion of thesignal at least in part by setting the second consecutive portion of thesignal to a modified value. A difference between the modified value anda value the first consecutive portion of the signal is less than adifference between the modified value and a value the second consecutiveportion of the signal. A difference between the modified value and avalue the third consecutive portion of the signal is less than thedifference between the modified value and the value the secondconsecutive portion of the signal.

Clause 44: In some examples of any of clauses 1-43, the processingcircuitry is configured to modify the at least one third portion of thesignal at least in part by setting the second consecutive portion of thesignal to a mean or a median of a value the first consecutive portion ofthe signal and a value the third consecutive portion of the signal.

Clause 45: In some examples of any of clauses 1-44, the processingcircuitry is configured to identify the at least one third portion atleast in part by determining a low-pass-filtered signal from the atleast one third portion of the signal and determining that a differencebetween a blood pressure value of the at least one third portion of thesignal and an associated blood pressure value of low-pass-filteredsignal is greater than a blood pressure variation value.

Clause 46: In some examples of any of clauses 1-45, the processingcircuitry is configured to determine the low-pass-filtered signal atleast in part by low-pass filtering the signal using a cutoff frequencyof between approximately ten hertz and approximately thirty hertz.

Clause 47: In some examples of any of clauses 1-46, the processingcircuitry is configured to determine that the difference between theblood pressure value of the at least one third portion of the signal andthe associated blood pressure value is greater than the blood pressurevariation value at least in part by determining that the differencebetween the blood pressure value of the at least one third portion ofthe signal and the associated blood pressure value is greater than threemillimeters of mercury.

Clause 48: In some examples of any of clauses 1-47, the blood pressurevariation value is greater than or equal to two millimeters of mercury.

Clause 49: In some examples of any of clauses 1-48, the processingcircuitry is configured to identify the at least one third portion atleast in part by determining that the signal has an invalid value withinthe at least one third portion for less than a threshold time duration.

Clause 50: In some examples of any of clauses 1-49, the threshold timeduration is less than or equal to one second.

Clause 51: In some examples of any of clauses 1-50, the processingcircuitry is configured to identify the at least one third portion atleast in part by determining that the third characteristic of the signalexceeds the third threshold for less than a threshold time duration.

Clause 52: In some examples of any of clauses 1-51, the threshold timeduration is less than or equal to one second.

Clause 53: In some examples of any of clauses 1-52, the processingcircuitry is configured to identify at least one fourth portion of thesignal including a fourth characteristic of the signal indicates thatthe patient is undergoing a cardiopulmonary bypass procedure. Theprocessing circuitry is also configured to determine a subset of the setof mean arterial pressure values for the at least one fourth portionbased on a moving average of the signal in response to determining thatthe fourth characteristic of the signal indicates that the patient isundergoing the cardiopulmonary bypass procedure.

Clause 54: In some examples of any of clauses 1-53, the processingcircuitry is configured to identify the at least one fourth portion atleast in part by determining that a mean power of the signal is lessthan a threshold power level.

Clause 55: In some examples of any of clauses 1-54, the processingcircuitry is configured to identify the at least one fourth portion atleast in part by determining that a diastolic value of the signal isless than a blood pressure threshold value.

Clause 56: In some examples of any of clauses 1-55, the processingcircuitry is configured to determine that the diastolic value is lessthan the blood pressure threshold value at least in part by determiningthat a percentile value is less than the blood pressure threshold value,where the percentile value is less than or equal to a fifteenthpercentile of the signal.

Clause 57: In some examples of any of clauses 1-56, the processingcircuitry is configured to identify the at least one fourth portion atleast in part by determining that a total power within a frequency bandis less than a threshold power level, the frequency band including anestimated heart rate frequency of the patient.

Clause 58: In some examples of any of clauses 1-57, the processingcircuitry is configured to identify the at least one fourth portion atleast in part by determining that a prediction value is greater than athreshold value.

Clause 59: In some examples of any of clauses 1-58, the processingcircuitry is configured to identify the at least one fourth portion atleast in part by determining that the at least one fourth portion of thesignal does not satisfy a first non-bypass condition and determiningthat the at least one fourth portion of the signal does not satisfy asecond non-bypass condition.

Clause 60: In some examples of any of clauses 1-59, the processingcircuitry is configured to determine the set of mean arterial pressurevalues at least in part by convolving a kernel and blood pressure valuesof the filtered signal and determining signal peaks based on convolvingthe kernel and the blood pressure values of the filtered signal.

Clause 61: In some examples of any of clauses 1-60, the processingcircuitry is configured to convolve the kernel and the blood pressurevalues of the filtered signal at least in part by convolving a firstkernel and the blood pressure values of the filtered signal to produce afirst convolved signal. The processing circuitry is configured todetermine the set of mean arterial pressure values at least in part byconvolving a second kernel and the blood pressure values of the filteredsignal and selecting one of the first convolved signal or the secondconvolved signal based on a power of the first convolved signal and apower of the second convolved signal. The processing circuitry isconfigured to determine the signal peaks at least in part by determiningthe signal peaks based on the selection of the first convolved signal orthe second convolved signal.

Clause 62: In some examples, a method includes receiving, by processingcircuitry, a signal indicative of a blood pressure of a patient andidentifying, by processing circuitry, at least one first portion of thesignal including a first characteristic of the signal exceeding a firstthreshold. The method also includes identifying, by processingcircuitry, at least one first portion of the signal including a secondcharacteristic of the signal exceeding a second threshold, the firstcharacteristic being different than the second characteristic. Themethod further includes determining, by the processing circuitry, afiltered signal indicative of the blood pressure of the patient byexcluding the at least one first portion and the at least one secondportion from the signal. The method includes determining, by processingcircuitry, a set of mean arterial pressure values based on the filteredsignal and determining, by processing circuitry, an autoregulationstatus of the patient based on the set of mean arterial pressure values.

Clause 63: In some examples of clause 62, the first characteristicincludes a blood pressure value.

Clause 64: In some examples of clause 62 or clause 63, identifying theat least one first portion of the signal includes determining that thefirst characteristic is one of less than a minimum blood pressure valueor greater than a maximum blood pressure value.

Clause 65: In some examples of any of clauses 62-64, determining thatthe first characteristic is less than the minimum blood pressure valueincludes determining that the blood pressure value of the patient isless than twenty millimeters of mercury.

Clause 66: In some examples of any of clauses 62-65, the minimum bloodpressure value is less than or equal to twenty-five millimeters ofmercury.

Clause 67: In some examples of any of clauses 62-66, determining thatthe first characteristic is greater than the maximum blood pressurevalue includes determining that the blood pressure value is greater thantwo hundred and fifty millimeters of mercury.

Clause 68: In some examples of any of clauses 62-67, the maximum bloodpressure value is greater than or equal to two hundred millimeters ofmercury.

Clause 69: In some examples of any of clauses 62-68, identifying the atleast one first portion of the signal includes determining that adifference in blood pressure between two consecutive portions of thesignal is greater than a blood pressure variation value.

Clause 70: In some examples of any of clauses 62-69, determining thatthe difference in blood pressure between the two consecutive portions isgreater than the blood pressure variation value includes determiningthat the difference in blood pressure between the two consecutiveportions is greater than fifty millimeters of mercury.

Clause 71: In some examples of any of clauses 62-70, the blood pressurevariation value is greater than or equal to forty millimeters ofmercury.

Clause 72: In some examples of any of clauses 62-71, identifying the atleast one first portion of the signal includes determining that aderivative of the signal exceeds a threshold rate for longer than athreshold time duration for a total change in the signal greater than ablood pressure variation value.

Clause 73: In some examples of any of clauses 62-72, determining thatthe derivative of the at least one first portion of the signal exceedsthe threshold rate for longer than the threshold time duration for atotal change greater than the blood pressure variation value includesdetermining that the derivative of the at least one first portion of thesignal is greater than negative ten millimeters of mercury per secondfor longer than the threshold time duration for the total change greaterthan the blood pressure variation value.

Clause 74: In some examples of any of clauses 62-73, the threshold rateis greater than or equal to negative fifteen millimeters of mercury persecond.

Clause 75: In some examples of any of clauses 62-74, determining thatthe derivative of the at least one first portion of the signal exceedsthe threshold rate for longer than the threshold time duration for atotal change greater than the blood pressure variation value includesdetermining that the derivative of the at least one first portion of thesignal is less than positive ten millimeters of mercury per second forlonger than the threshold time duration for the total change greaterthan the blood pressure variation value.

Clause 76: In some examples of any of clauses 62-75, the threshold rateis less than or equal to positive fifteen millimeters of mercury persecond.

Clause 77: In some examples of any of clauses 62-76, determining thatthe derivative of the at least one first portion of the signal exceedsthe threshold rate for longer than the threshold time duration for atotal change greater than the blood pressure variation value includesdetermining that the derivative of the at least one first portion of thesignal exceeds the threshold rate for longer than one second for thetotal change greater than the blood pressure variation value.

Clause 78: In some examples of any of clauses 62-77, the threshold timeduration is greater than or equal to five hundred milliseconds.

Clause 79: In some examples of any of clauses 62-78, determining thatthe derivative of the at least one first portion of the signal exceedsthe threshold rate for longer than the threshold time duration for atotal change greater than the blood pressure variation value includesdetermining that the derivative of the at least one first portion of thesignal exceeds the threshold rate for longer than the threshold timeduration for the total change greater than twenty millimeters ofmercury.

Clause 80: In some examples of any of clauses 62-79, the blood pressurevariation value is greater than or equal to fifteen millimeters ofmercury.

Clause 81: In some examples of any of clauses 62-80, identifying the atleast one first portion of the signal includes determining that a numberof consecutive samples of the blood pressure of the patient are within apredetermined range of deviation.

Clause 82: In some examples of any of clauses 62-81, determining thatthe number of consecutive samples are within the predetermined range ofvariation includes determining that five consecutive samples are withina range of variation of one millimeter of mercury.

Clause 83: In some examples of any of clauses 62-82, the predeterminedrange of variation is less than or equal to two millimeters of mercury.

Clause 84: In some examples of any of clauses 62-83, identifying the atleast one first portion of the signal includes determining that amonotonic change in the blood pressure of the patient of the signal isgreater than a blood pressure variation value.

Clause 85: In some examples of any of clauses 62-84, determining thatthe monotonic change is greater than a blood pressure variation valueincludes determining that the monotonic change is greater than onehundred and sixty millimeters of mercury.

Clause 86: In some examples of any of clauses 62-85, the blood pressurevariation value is greater than or equal to one hundred and fortymillimeters of mercury.

Clause 87: In some examples of any of clauses 62-86, identifying the atleast one first portion includes determining that the monotonic changeoccurs for more than one second.

Clause 88: In some examples of any of clauses 62-87, identifying the atleast one first portion includes determining that the monotonic changeoccurs for more than a threshold time duration, where the threshold timeduration is greater than or equal to eight hundred milliseconds.

Clause 89: In some examples of any of clauses 62-88, identifying the atleast one first portion of the signal includes determining that a levelof noise of the signal is greater than the first threshold level.

Clause 90: In some examples of any of clauses 62-89, determining thatthe level of noise of the signal is greater than the first thresholdlevel includes determining that a standard deviation of a derivative ofthe signal is greater than fifteen millimeters of mercury per second.

Clause 91: In some examples of any of clauses 62-90, determining thatthe level of noise of the signal is greater than the first thresholdlevel includes determining that a standard deviation of a derivative ofthe signal is greater than a threshold rate, where the threshold rate isgreater than or equal to twelve millimeters of mercury per second.

Clause 92: In some examples of any of clauses 62-91, identifying the atleast one first portion of the signal includes determining that adiastolic value of the signal is greater than a blood pressure thresholdvalue.

Clause 93: In some examples of any of clauses 62-92, determining thatthe diastolic value is greater than the blood pressure threshold valueincludes determining that the diastolic value of the signal is greaterthan one hundred and thirty millimeters of mercury.

Clause 94: In some examples of any of clauses 62-93, the blood pressurethreshold value is greater than or equal to one hundred and twentymillimeters of mercury.

Clause 95: In some examples of any of clauses 62-94, determining thatthe diastolic value within the first portion is greater than the bloodpressure threshold value includes determining that a percentile value isgreater than the blood pressure threshold value.

Clause 96: In some examples of any of clauses 62-95, the percentilevalue is a tenth percentile of the signal.

Clause 97: In some examples of any of clauses 62-96, the percentilevalue is less than or equal to a fifteenth percentile of the signal.

Clause 98: In some examples of any of clauses 62-97, identifying the atleast one first portion includes determining that a diastolic value ofthe signal is greater than a blood pressure threshold value for athreshold time duration.

Clause 99: In some examples of any of clauses 62-98, the threshold timeduration is less than or equal to two seconds.

Clause 100: In some examples of any of clauses 62-99, further includingidentifying at least one third portion of the signal including a thirdcharacteristic of the signal exceeding a third threshold.

Clause 101: In some examples of any of clauses 62-100, determining thefiltered signal includes modifying the at least one third portion of thesignal.

Clause 102: In some examples of any of clauses 62-101, identifying theat least one third portion includes determining that a difference inblood pressure between a first consecutive portion of the signal and asecond consecutive portion of the signal is greater than a first bloodpressure variation value and determining that a difference in bloodpressure between the second consecutive portion of the signal and athird consecutive portion of the signal is greater than a second bloodpressure variation value.

Clause 103: In some examples of any of clauses 62-102, determining thatthe first sample is greater than the second sample by at least the firstblood pressure threshold value includes determining that the firstsample is greater than the second sample by at least twenty millimetersof mercury, and determining that the second sample is greater than thethird sample by at least the second blood pressure threshold valueincludes determining that the second sample is greater than the thirdsample by at least twenty millimeters of mercury.

Clause 104: In some examples of any of clauses 62-103, modifying the atleast one third portion of the signal includes setting the secondconsecutive portion of the signal to a modified value. A differencebetween the modified value and a value the first consecutive portion ofthe signal is less than a difference between the modified value and avalue the second consecutive portion of the signal. A difference betweenthe modified value and a value the third consecutive portion of thesignal is less than the difference between the modified value and thevalue the second consecutive portion of the signal.

Clause 105: In some examples of any of clauses 62-104, modifying the atleast one third portion of the signal includes setting the secondconsecutive portion of the signal to a mean or a median of a value thefirst consecutive portion of the signal and a value the thirdconsecutive portion of the signal.

Clause 106: In some examples of any of clauses 62-105, identifying theat least one third portion includes determining a low-pass-filteredsignal from the at least one third portion of the signal and determiningthat a difference between a blood pressure value of the at least onethird portion of the signal and an associated blood pressure value oflow-pass-filtered signal is greater than a blood pressure variationvalue.

Clause 107: In some examples of any of clauses 62-106, determining thelow-pass-filtered signal includes low-pass filtering the signal using acutoff frequency of between approximately ten hertz and approximatelythirty hertz.

Clause 108: In some examples of any of clauses 62-107, determining thatthe difference between the blood pressure value of the at least onethird portion of the signal and the associated blood pressure value isgreater than the blood pressure variation value includes determiningthat the difference between the blood pressure value of the at least onethird portion of the signal and the associated blood pressure value isgreater than three millimeters of mercury.

Clause 109: In some examples of any of clauses 62-108, the bloodpressure variation value is greater than or equal to two millimeters ofmercury.

Clause 110: In some examples of any of clauses 62-109, identifying theat least one third portion includes determining that the signal has aninvalid value within the at least one third portion for less than athreshold time duration.

Clause 111: In some examples of any of clauses 62-110, the thresholdtime duration is less than or equal to one second.

Clause 112: In some examples of any of clauses 62-111, identifying theat least one third portion includes determining that the thirdcharacteristic of the signal exceeds the third threshold for less than athreshold time duration.

Clause 113: In some examples of any of clauses 62-112, the thresholdtime duration is less than or equal to one second.

Clause 114: In some examples of any of clauses 62-113, identifying atleast one fourth portion of the signal including a fourth characteristicof the signal indicates that the patient is undergoing a cardiopulmonarybypass procedure. In addition, determining a subset of the set of meanarterial pressure values for the at least one fourth portion based on amoving average of the signal is in response to determining that thefourth characteristic of the signal indicates that the patient isundergoing the cardiopulmonary bypass procedure.

Clause 115: In some examples of any of clauses 62-114, identifying theat least one fourth portion includes determining that a mean power ofthe signal is less than a threshold power level.

Clause 116: In some examples of any of clauses 62-115, identifying theat least one fourth portion includes determining that a diastolic valueof the signal is less than a blood pressure threshold value.

Clause 117: In some examples of any of clauses 62-116, determining thatthe diastolic value is less than the blood pressure threshold valueincludes determining that a percentile value is less than the bloodpressure threshold value, where the percentile value is less than orequal to a fifteenth percentile of the signal.

Clause 118: In some examples of any of clauses 62-117, identifying theat least one fourth portion includes determining that a total powerwithin a frequency band is less than a threshold power level, thefrequency band including an estimated heart rate frequency of thepatient.

Clause 119: In some examples of any of clauses 62-118, identifying theat least one fourth portion includes determining that a prediction valueis greater than a threshold value.

Clause 120: In some examples of any of clauses 62-119, identifying theat least one fourth portion includes determining that the at least onefourth portion of the signal does not satisfy a first non-bypasscondition and determining that the at least one fourth portion of thesignal does not satisfy a second non-bypass condition.

Clause 121: In some examples of any of clauses 62-120, determining theset of mean arterial pressure values includes convolving a kernel andblood pressure values of the filtered signal and determining signalpeaks based on convolving the kernel and the blood pressure values ofthe filtered signal.

Clause 122: In some examples of any of clauses 62-121, convolving thekernel and the blood pressure values of the filtered signal includesconvolving a first kernel and the blood pressure values of the filteredsignal to produce a first convolved signal. Determining the set of meanarterial pressure values includes convolving a second kernel and theblood pressure values of the filtered signal and selecting one of thefirst convolved signal or the second convolved signal based on a powerof the first convolved signal and a power of the second convolvedsignal. Determining the signal peaks includes determining the signalpeaks based on the selection of the first convolved signal or the secondconvolved signal.

Clause 123: In some examples, a device includes a display, sensingcircuitry configured to generate a signal indicative of the bloodpressure of the patient, and a memory configured to store a firstthreshold for a first characteristic of the signal and a secondthreshold for a second characteristic of the signal. The device alsoincludes processing circuitry configured to identify at least one firstportion of the signal including the first characteristic of the signalexceeding the first threshold. The processing circuitry is alsoconfigured to identify at least one second portion of the signalincluding the second characteristic of the signal exceeding the secondthreshold, the first characteristic being different than the secondcharacteristic. The processing circuitry is further configured todetermine a filtered signal indicative of the blood pressure of thepatient by excluding the at least one first portion and the at least onesecond portion from the signal. The processing circuitry is configuredto determine a set of mean arterial pressure values based on thefiltered signal and determine an autoregulation status of the patientbased on the set of mean arterial pressure values.

Clause 124: In some examples of clause 123, the processing circuitry isconfigured to perform the method of clauses 62-122 or any combinationthereof.

Clause 125: In some examples, a device includes a computer-readablemedium having executable instructions stored thereon, configured to beexecutable by processing circuitry for causing the processing circuitryto receive a signal indicative of a blood pressure of the patient andidentify at least one first portion of the signal including a firstcharacteristic of the signal exceeding a first threshold. Theinstructions further cause the processing circuitry to identify at leastone second portion of the signal including a second characteristic ofthe signal exceeding a second threshold, the first characteristic beingdifferent than the second characteristic. The instructions also causethe processing circuitry to determine a filtered signal indicative ofthe blood pressure of the patient by excluding the at least one firstportion and the at least one second portion from the signal. Theinstructions also cause the processing circuitry to determine a set ofmean arterial pressure values based on the filtered signal and determinean autoregulation status of the patient based on the set of meanarterial pressure values.

Clause 126: In some examples of clause 125, the instructions furthercause the processing circuitry to perform the method of clauses 62-122or any combination thereof.

Clause 127: In some examples, a method includes receiving, by processingcircuitry, a signal indicative of a blood pressure of a patient andidentifying, by processing circuitry, at least one first portion of thesignal including a first characteristic of the signal exceeding a firstthreshold. The method also includes identifying, by processingcircuitry, at least one first portion of the signal including a secondcharacteristic of the signal exceeding a second threshold, the firstcharacteristic being different than the second characteristic. Themethod further includes determining, by the processing circuitry, afiltered signal indicative of the blood pressure of the patient bymodifying the at least one first portion and the at least one secondportion from the signal. The method includes determining, by processingcircuitry, a set of mean arterial pressure values based on the filteredsignal and determining, by processing circuitry, an autoregulationstatus of the patient based on the set of mean arterial pressure values.

Clause 128: In some examples, a device includes processing circuitryconfigured to perform the method of clause 62-122 or any combinationthereof.

Clause 129: In some examples, a method includes receiving, by processingcircuitry, a signal indicative of a blood pressure of a patient andidentifying, by processing circuitry, at least one portion of the signalincluding a characteristic of the signal indicating that the patient isundergoing a cardiopulmonary bypass procedure. The method furtherincludes determining, by the processing circuitry, a set of meanarterial pressure values based on the signal and determining, byprocessing circuitry, a subset of the set of mean arterial pressurevalues for the at least one portion of the signal based on a movingaverage of the signal in response to determining that the characteristicof the signal indicates that the patient is undergoing thecardiopulmonary bypass procedure. The method includes determining, byprocessing circuitry, an autoregulation status of the patient based onthe set of mean arterial pressure values and the subset of the set ofmean arterial pressure values for the at least one portion of thesignal.

Clause 130: In some examples, a device includes processing circuitryconfigured to perform the method of clause 62-122 or any combinationthereof.

Clause 131: In some examples, a method includes receiving, by processingcircuitry, a signal indicative of a blood pressure of a patient andconvolving, by processing circuitry, a kernel and blood pressure valuesof the signal. The method also includes determining, by processingcircuitry, signal peaks based on convolving the kernel and the bloodpressure values of the signal. The method further includes determining,by the processing circuitry, a set of mean arterial pressure valuesbased on the blood pressure values of the signal and the signal peaks.The method includes determining, by processing circuitry, anautoregulation status of the patient based on the set of mean arterialpressure values.

Clause 132: In some examples, a device includes processing circuitryconfigured to perform the method of clause 62-122 or any combinationthereof.

Clause 133: In some examples, a device includes means for receiving asignal indicative of a blood pressure of the patient and means foridentifying at least one first portion of the signal including a firstcharacteristic of the signal exceeding a first threshold. The devicealso includes means for identifying at least one second portion of thesignal including a second characteristic of the signal exceeding asecond threshold, the first characteristic being different than thesecond characteristic. The device includes means for determining afiltered signal indicative of the blood pressure of the patient byexcluding the at least one first portion and the at least one secondportion from the signal. The device further includes means fordetermining a set of mean arterial pressure values based on the filteredsignal and means for determining an autoregulation status of the patientbased on the set of mean arterial pressure values.

The disclosure contemplates computer-readable storage media comprisinginstructions to cause a processor to perform any of the functions andtechniques described herein. The computer-readable storage media maytake the example form of any volatile, non-volatile, magnetic, optical,or electrical media, such as a RAM, ROM, NVRAM, EEPROM, or flash memory.The computer-readable storage media may be referred to asnon-transitory. A programmer, such as patient programmer or clinicianprogrammer, or other computing device may also contain a more portableremovable memory type to enable easy data transfer or offline dataanalysis.

The techniques described in this disclosure, including those attributedto devices 100 and 200, processing circuitry 110, 210, 214, and 216,memories 120 and 220, displays 132 and 232, sensing circuitries 140-142,circuitries 240 and 245, sensing devices 150-152 and 250, and variousconstituent components, may be implemented, at least in part, inhardware, software, firmware or any combination thereof. For example,various aspects of the techniques may be implemented within one or moreprocessors, including one or more microprocessors, DSPs, ASICs, FPGAs,or any other equivalent integrated or discrete logic circuitry, as wellas any combinations of such components, embodied in programmers, such asphysician or patient programmers, stimulators, remote servers, or otherdevices. The term “processor” or “processing circuitry” may generallyrefer to any of the foregoing logic circuitry, alone or in combinationwith other logic circuitry, or any other equivalent circuitry.

As used herein, the term “circuitry” refers to an ASIC, an electroniccircuit, a processor (shared, dedicated, or group) and memory thatexecute one or more software or firmware programs, a combinational logiccircuit, or other suitable components that provide the describedfunctionality. The term “processing circuitry” refers one or moreprocessors distributed across one or more devices. For example,“processing circuitry” can include a single processor or multipleprocessors on a device. “Processing circuitry” can also includeprocessors on multiple devices, wherein the operations described hereinmay be distributed across the processors and devices.

Such hardware, software, firmware may be implemented within the samedevice or within separate devices to support the various operations andfunctions described in this disclosure. For example, any of thetechniques or processes described herein may be performed within onedevice or at least partially distributed amongst two or more devices,such as between devices 100 and 200, processing circuitry 110, 210, 214,and 216, memories 120 and 220, sensing circuitries 140-142, and/orcircuitries 240 and 245. In addition, any of the described units,modules or components may be implemented together or separately asdiscrete but interoperable logic devices. Depiction of differentfeatures as modules or units is intended to highlight differentfunctional aspects and does not necessarily imply that such modules orunits must be realized by separate hardware or software components.Rather, functionality associated with one or more modules or units maybe performed by separate hardware or software components, or integratedwithin common or separate hardware or software components.

The techniques described in this disclosure may also be embodied orencoded in an article of manufacture including a non-transitorycomputer-readable storage medium encoded with instructions. Instructionsembedded or encoded in an article of manufacture including anon-transitory computer-readable storage medium encoded, may cause oneor more programmable processors, or other processors, to implement oneor more of the techniques described herein, such as when instructionsincluded or encoded in the non-transitory computer-readable storagemedium are executed by the one or more processors. Examplenon-transitory computer-readable storage media may include RAM, ROM,programmable ROM (PROM), erasable programmable ROM (EPROM),electronically erasable programmable ROM (EEPROM), flash memory, a harddisk, a compact disc ROM (CD-ROM), a floppy disk, a cassette, magneticmedia, optical media, or any other computer readable storage devices ortangible computer readable media.

In some examples, a computer-readable storage medium comprisesnon-transitory medium. The term “non-transitory” may indicate that thestorage medium is not embodied in a carrier wave or a propagated signal.In certain examples, a non-transitory storage medium may store data thatcan, over time, change (e.g., in RAM or cache). Elements of devices andcircuitry described herein, including, but not limited to, devices 100and 200, processing circuitry 110, 210, 214, and 216, memories 120 and220, displays 132 and 232, sensing circuitries 140-142, circuitries 240and 245, sensing devices 150-152 and 250 may be programmed with variousforms of software. The one or more processors may be implemented atleast in part as, or include, one or more executable applications,application modules, libraries, classes, methods, objects, routines,subroutines, firmware, and/or embedded code, for example.

In examples in which processing circuitry 110 is described herein asdetermining that a value is less than or equal to another value, thisdescription may also include processing circuitry 110 determining that avalue is only less than the other value. Similarly, in examples in whichprocessing circuitry 110 is described herein as determining that a valueis less than another value, this description may also include processingcircuitry 110 determining that a value is less than or equal to theother value. The same properties may also apply to the terms “greaterthan” and “greater than or equal to.”

Various examples of the disclosure have been described. Any combinationof the described systems, operations, or functions is contemplated.These and other examples are within the scope of the following claims.

What is claimed is:
 1. A device comprising: processing circuitryconfigured to: receive a signal indicative of a blood pressure of apatient; identify at least one first portion of the signal comprising afirst characteristic of the signal exceeding a first threshold; identifyat least one second portion of the signal comprising a secondcharacteristic of the signal exceeding a second threshold, the firstcharacteristic being different than the second characteristic; determinea filtered signal indicative of the blood pressure of the patient byexcluding the at least one first portion and the at least one secondportion from the signal; determine a set of mean arterial pressurevalues based on the filtered signal; and determine an autoregulationstatus of the patient based on the set of mean arterial pressure values.2. The device of claim 1, wherein the first characteristic comprises ablood pressure value, and wherein the processing circuitry is configuredto identify the at least one first portion of the signal at least inpart by determining that the first characteristic is one of less than aminimum blood pressure value or greater than a maximum blood pressurevalue.
 3. The device of claim 1, wherein the processing circuitry isconfigured to identify the at least one first portion of the signal atleast in part by determining that a difference in blood pressure betweentwo consecutive portions of the signal is greater than a blood pressurevariation value.
 4. The device of claim 1, wherein the processingcircuitry is configured to identify the at least one first portion ofthe signal at least in part by determining that a derivative of thesignal exceeds a threshold rate for longer than a threshold timeduration for a total change in the signal greater than a blood pressurevariation value.
 5. The device of claim 1, wherein the processingcircuitry is configured to identify the at least one first portion ofthe signal at least in part by determining that a number of consecutivesamples of the blood pressure of the patient are within a predeterminedrange of deviation.
 6. The device of claim 1, wherein the processingcircuitry is configured to identify the at least one first portion ofthe signal at least in part by determining that a monotonic change inthe blood pressure of the patient of the signal is greater than a bloodpressure variation value.
 7. The device of claim 1, wherein theprocessing circuitry is configured to identify the at least one firstportion of the signal at least in part by determining that a level ofnoise of the signal is greater than the first threshold level.
 8. Thedevice of claim 1, wherein the processing circuitry is configured toidentify the at least one first portion of the signal at least in partby determining that a diastolic value of the signal is greater than ablood pressure threshold value.
 9. The device of claim 1, wherein theprocessing circuitry is further configured to identify at least onethird portion of the signal comprising a third characteristic of thesignal exceeding a third threshold, and wherein the processing circuitryis configured to determine the filtered signal at least in part bymodifying the at least one third portion of the signal.
 10. The deviceof claim 9, wherein the processing circuitry is configured to identifythe at least one third portion of the signal at least in part by:determining that a difference in blood pressure between a firstconsecutive portion of the signal and a second consecutive portion ofthe signal is greater than a first blood pressure variation value; anddetermining that a difference in blood pressure between the secondconsecutive portion of the signal and a third consecutive portion of thesignal is greater than a second blood pressure variation value, whereinthe processing circuitry is configured to modify the at least one thirdportion of the signal at least in part by setting the second consecutiveportion of the signal to a modified value closer to the first and thirdconsecutive portions of the signal than to an unmodified value of thesecond consecutive portion of the signal.
 11. The device of claim 1,wherein the processing circuitry is further configured to: identify atleast one fourth portion of the signal comprising a fourthcharacteristic of the signal indicates that the patient is undergoing acardiopulmonary bypass procedure; and determine a subset of the set ofmean arterial pressure values for the at least one fourth portion basedon a moving average of the signal in response to determining that thefourth characteristic of the signal indicates that the patient isundergoing the cardiopulmonary bypass procedure.
 12. The device of claim11, wherein the processing circuitry is configured to identify the atleast one fourth portion at least in part by determining that a totalpower within a frequency band is less than a threshold power level, thefrequency band including an estimated heart rate frequency of thepatient.
 13. The device of claim 1, wherein the processing circuitry isconfigured to determine the set of mean arterial pressure values atleast in part by: convolving a kernel and blood pressure values of thefiltered signal; and determining signal peaks based on convolving thekernel and the blood pressure values of the filtered signal.
 14. Thedevice of claim 13, wherein the processing circuitry is configured toconvolve the kernel and the blood pressure values of the filtered signalat least in part by convolving a first kernel and the blood pressurevalues of the filtered signal to produce a first convolved signal,wherein the processing circuitry is configured to determine the set ofmean arterial pressure values at least in part by: convolving a secondkernel and the blood pressure values of the filtered signal; andselecting one of the first convolved signal or the second convolvedsignal based on a power of the first convolved signal and a power of thesecond convolved signal, and wherein the processing circuitry isconfigured to determine the signal peaks at least in part by determiningthe signal peaks based on the selection of the first convolved signal orthe second convolved signal.
 15. A method comprising: receiving, byprocessing circuitry, a signal indicative of a blood pressure of apatient; identifying, by the processing circuitry, at least one firstportion of the signal comprising a first characteristic of the signalexceeding a first threshold; identifying, by the processing circuitry,at least one first portion of the signal comprising a secondcharacteristic of the signal exceeding a second threshold, the firstcharacteristic being different than the second characteristic;determining, by the processing circuitry, a filtered signal indicativeof the blood pressure of the patient by excluding the at least one firstportion and the at least one second portion from the signal;determining, by the processing circuitry, a set of mean arterialpressure values based on the filtered signal; and determining, by theprocessing circuitry, an autoregulation status of the patient based onthe set of mean arterial pressure values.
 16. The method of claim 15,further comprising identifying, by the processing circuitry, at leastone third portion of the signal comprising a third characteristic of thesignal exceeding a third threshold, wherein determining the filteredsignal comprises modifying the at least one third portion of the signal.17. The method of claim 15, further comprising: identifying, by theprocessing circuitry, at least one fourth portion of the signalcomprising a fourth characteristic of the signal indicates that thepatient is undergoing a cardiopulmonary bypass procedure; anddetermining, by the processing circuitry, a subset of the set of meanarterial pressure values for the at least one fourth portion based on amoving average of the signal in response to determining that the fourthcharacteristic of the signal indicates that the patient is undergoingthe cardiopulmonary bypass procedure.
 18. A device comprising acomputer-readable medium having executable instructions stored thereon,configured to be executable by processing circuitry for causing theprocessing circuitry to: receive a signal indicative of a blood pressureof a patient; identify at least one first portion of the signalcomprising a first characteristic of the signal exceeding a firstthreshold; identify at least one second portion of the signal comprisinga second characteristic of the signal exceeding a second threshold;determine a filtered signal indicative of the blood pressure of thepatient by excluding the at least one first portion and the at least onesecond portion from the signal; determine a set of mean arterialpressure values based on the filtered signal; and determine anautoregulation status of the patient based on the set of mean arterialpressure values.
 19. The method of claim 18, wherein the instructionsare further configured to cause the processing circuitry to identify atleast one third portion of the signal comprising a third characteristicof the signal exceeding a third threshold, and wherein the instructionsto determine the filtered signal comprise instructions to modify the atleast one third portion of the signal.
 20. The method of claim 18,wherein the instructions are further configured to cause the processingcircuitry to: identify at least one fourth portion of the signalcomprising a fourth characteristic of the signal indicates that thepatient is undergoing a cardiopulmonary bypass procedure; and determinea subset of the set of mean arterial pressure values for the at leastone fourth portion based on a moving average of the signal in responseto determining that the fourth characteristic of the signal indicatesthat the patient is undergoing the cardiopulmonary bypass procedure.